Study on Risk Factors and Receiver Operator Characteristic Curve of Iatrogenic Withdrawal Syndrome in Pediatric Intensive Care Units: A Retrospective Study.

INTRODUCTION: The aims of this study were to investigate the independent risk factors associated with iatrogenic withdrawal syndrome in pediatric intensive care units (PICUs) and to establish receiver operator characteristic (ROC) curve to facilitate the diagnosis of iatrogenic withdrawal syndrome in clinical settings. METHODS: Pediatric patients who received analgesic and sedative medication at a tertiary hospital in the southern Zhejiang region of China between January 2016 and December 2022 were selected for the study. Clinical case data were retrospectively analyzed to gather information including age, gender, weight, total dose of analgesic and sedative medication, total treatment duration, average maintenance dose, and other relevant parameters. Medically induced withdrawal symptom scores were assessed using the Sophia Observation Scale for Withdrawal Symptoms (SOS). Univariate and multivariate logistic regression analyses were conducted on the above indicators to identify the risk factors for iatrogenic withdrawal, and an ROC curve was constructed. RESULTS: The study encompassed a total of 104 pediatric patients, comprising 47 patients in the SOS score ≥4 group and 57 patients in the SOS score ≤3 group. The incidence of iatrogenic withdrawal was 45.19%. Univariate analysis identified cumulative total dose of fentanyl, average daily dose of fentanyl, average daily dose of midazolam, and patient weight (p < 0.05) as factors associated with iatrogenic withdrawal syndrome. The logistic multiple regression analysis revealed that the average daily dose of fentanyl was an independent risk factor for the occurrence of iatrogenic withdrawal syndrome in critically ill children (p < 0.05). ROC curve analysis indicated an area under the curve of 0.711 (95% CI: 0.610-0.811) with sensitivity and specificity of 73.7% and 61.7%, respectively. CONCLUSION: The average daily maintenance dose of fentanyl holds significant clinical value in diagnosing and evaluating the prognosis of iatrogenic withdrawal syndrome and can provide a scientific foundation for enhancing sedative and analgesic management in clinical practice.

Evaluating the predictive performance of gut microbiota for the early-stage colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) has been regarded as one of the most frequently diagnosed malignancies among the leading causes of cancer-related morbidity and mortality globally. Diagnosis of CRC at the early-stages of tumour might improve the survival rate of patients. The current study sought to determine the performance of fecal Fusobacterium nucleatum (F. nucleatum) and Streptococcus bovis (S. bovis) for timely predicting CRC. METHODS: Through a case-control study, the fecal sample information of 83 individuals (38 females, 45 males) referring to a hospital in Tehran, Iran was used. All patients underwent a complete colonoscopy, regarded as a gold standard test. Bacterial species including S. bovis and F. nucleatum were measured by absolute quantitative real-time PCR. The Bayesian univariate and bivariate latent class models (LCMs) were applied to estimate the ability of the candidate bacterial markers in order to early detection of patients with CRC. RESULTS: Bayesian univariate LCMs demonstrated that the sensitivities of S. bovis and F. nucleatum were estimated to be 86% [95% credible interval (CrI) 0.82-0.91] and 82% (95% CrI 0.75-0.88); while specificities were 84% (95% CrI 0.78-0.89) and 80% (95% CrI 0.73-0.87), respectively. Moreover, the area under the receiver operating characteristic curves (AUCs) were 0.88 (95% CrI 0.83-0.94) and 0.80 (95% CrI 0.73-0.85) respectively for S. bovis and F. nucleatum. Based on the Bayesian bivariate LCMs, the sensitivities of S. bovis and F. nucleatum were calculated as 93% (95% CrI 0.84-0.98) and 90% (95% CrI 0.85-0.97), the specificities were 88% (95% CrI 0.78-0.93) and 87% (95% CrI 0.79-0.94); and the AUCs were 0.91 (95% CrI 0.83-0.99) and 0.88(95% CrI 0.81-0.96), respectively. CONCLUSIONS: Our data has identified that according to the Bayesian bivariate LCM, S. bovis and F. nucleatum had a more significant predictive accuracy compared with the univariate model. In summary, these intestinal bacteria have been highlighted as novel tools for early-stage CRC diagnosis.

BCL-xL is correlated with disease severity in neonatal infants with early sepsis.

BACKGROUND: Sepsis is the most common cause of morbidity and mortality in neonatal infants. It is essential to find an accurate and sensitive biomarker to confirm and treat neonatal sepsis in order to decrease the rate of mortality. The aim of this study was to investigate the association between disease severity in patients with sepsis and TNF-α, B cell lymphoma-extra-large (BCL-xL), and serum Mitochondrial membrane potential (MMP). METHODS: We investigated the correlation between SNAP-II score and levels of TNF-α, BCL-xL, and MMP-index, respectively. The receiver-operating characteristics (ROC) was to assess the diagnostic value of the the Bcl-xL in the diagnosis of the of septic shock. RESULTS: A total of 37 infants were diagnosed with sepsis. SNAP-II was positively correlated with the level of BCL-xL (r = 0.450, P = 0.006). The area under the BCL-xL curve was 83.0 %, and the 95 % CI was 67.1-93.3 %. The septic shock threshold was > 3.022 ng/mL, and the sensitivity and specificity were 75.0 and 95.2 %, respectively. The positive predictive value was 92.3 %, and the negative predictive value was 83.3 %. Furthermore, the level of SNAP-II was > 10, and BCL-xL was > 3.022 ng/mL as the threshold, and the sensitivity, specificity, positive predictive value, and negative predictive value of septic shock were 93.8 %, 95.2 %, 93.8 %, and 95.2 %, respectively. CONCLUSIONS: BCL-xL is associated with the progression of sepsis. The combination of BCL-xL and SNAP-II could be early predicte the severity of the disease.

Gut dysbiosis is associated with primary hypothyroidism with interaction on gut-thyroid axis.

Background Previous studies have shown that the gut microbiome is associated with thyroid diseases, including Graves' disease, Hashimoto's disease, thyroid nodules, and thyroid cancer. However, the association between intestinal flora and primary hypothyroidism remains elusive. We aimed to characterize gut microbiome in primary hypothyroidism patients. Methods Fifty-two primary hypothyroidism patients and 40 healthy controls were recruited. The differences in gut microbiota between the two groups were analyzed by 16S rRNA sequencing technology. Fecal microbiota transplantation (FMT) was performed in mice using flora from both groups; changes in thyroid function were then assessed in the mice. Results There were significant differences in α and ß diversities of gut microbiota between primary hypothyroidism patients and healthy individuals. The random forest analysis indicated that four intestinal bacteria (Veillonella, Paraprevotella, Neisseria, and Rheinheimera) could distinguish untreated primary hypothyroidism patients from healthy individuals with the highest accuracy; this was confirmed by receiver operator characteristic curve analysis. The short chain fatty acid producing ability of the primary hypothyroidism patients' gut was significantly decreased, which resulted in the increased serum lipopolysaccharide (LPS) levels. The FMT showed that mice receiving the transplant from primary hypothyroidism patients displayed decreased total thyroxine levels. Conclusions Our study suggests that primary hypothyroidism causes changes in gut microbiome. In turn, an altered flora can affect thyroid function in mice. These findings could help understand the development of primary hypothyroidism and might be further used to develop potential probiotics to facilitate the adjuvant treatment of this disease.

Risk factors for hospitalization in youth with type 1 diabetes: Development and validation of a multivariable prediction model.

OBJECTIVE: To develop a multivariable prediction model to identify patients with type 1 diabetes at increased risk of hospitalization for diabetic ketoacidosis or hyperglycemia with ketosis in the 12 months following assessment. METHODS: Retrospective review of clinical data from patients with type 1 diabetes less than 17 years old at a large academic children's hospital (5732 patient years, 652 admissions). Data from the previous 12 months were assessed on October 15, 2015, 2016, 2017, and 2018, and used to predict hospitalization in the following 12 months using generalized estimating equations. Variables that were significant predictors of hospitalization in univariate analyses were entered into a multivariable model. 2014 to 2016 data were used as a training dataset, and 2017 to 2019 data for validation. Discrimination of the model was assessed with receiver operator characteristic curves. RESULTS: Admission in the preceding year, hemoglobin (Hb)A1c, non-commercial insurance, female sex, and non-White race were all individual predictors of hospitalization, but age, duration of diabetes and number of office visits in the preceding year were not. In multivariable analysis with threshold P < .0033, admissions in the previous 12 months, HbA1c, and non-commercial insurance remained as significant predictors. The model identified a subset of ~8% of the patients with a collective 42% risk of hospitalization, thus increased 5-fold compared with the 8% risk of hospitalization in the remaining 93% of patients. Similar results were obtained with the validation dataset. CONCLUSION: Our multivariable prediction model identified patients at increased risk of admission in the 12 months following assessment.

A new method to define cutoff values in nerve conduction studies for carpal tunnel syndrome considering the presence of false-positive cases.

BACKGROUND: Nerve conduction studies (NCS) are useful tools for diagnosing carpal tunnel syndrome (CTS). Establishing the normal values is the first step required for utilizing NCS for diagnosis. Previous epidemiological studies demonstrated the presence of fairly large number of false-positive subjects regarding NCS among control population, which has not been properly considered in past studies. This study proposed a new method to address this issue. METHODS: Non-diabetic 144 CTS patients were retrospectively enrolled using clinically defined inclusion criteria. Controls consisted of 73 age-matched volunteers without hand symptoms. Six NCS parameters were evaluated including peak-latency difference by the thumb method (thumbdif) and that by the ring-finger method (ringdif). The Youden index of the receiver operator characteristic curve was used both to judge the sensitivity of a parameter and to identify false-positive cases that were thought to have subclinical median neuropathy at the wrist. The linear function of six parameters was constructed, and the coefficient for each parameter was variously changed. RESULTS: When the Youden index took on the maximum value, seven control subjects (10%) were identified as false-positive and were excluded from the calculation of normal values. The most sensitive parameter before exclusion was thumbdif, whereas ringdif became the most sensitive after exclusion. The cut-off value for ringdif was 1.15 ms before exclusion, but was 0.37 ms after exclusion. CONCLUSION: This method can be widely applied to solve the statistical problem when the gold standard is lacking, and the outside reference standard is not completely reliable.

[Detection of neuron-specific enolase in patients with subacute 1, 2-dichloroethane poisoning].

Objective: To investigate the changes of neuron-specific enolase (NSE) in serum and cerebrospinal fluid of patients with subacute 1, 2-dichloroethane (DCE) poisoning. Methods: Ten patients with subacute 1, 2-DCE poisoning hospitalized in Guangzhou 12th Municipal People's Hospital from December 2014 to March 2019 were taken as the subacute 1, 2-DCE poisoning group, 34 typical acute toxic encephalopathy patients hospitalized at the same time as typical acute toxic encephalopathy group, 40 healthy physical examinees as normal control group. The levels of serum NSE in patients of subacute 1, 2-DCE poisoning and typical acute toxic encephalopathy group during onset and improvement were detected by chemiluminescence method, and the results were analyzed statistically. The level of NSE in cerebrospinal fluid of subacute 1, 2-DCE poisoning group was detected and analyzed its correlation with the level of NSE in serum. Using receiver operator characteristic (ROC) curve to analyze the diagnostic efficacy of NSE in subacute 1, 2-DCE poisoning and typical acute toxic encephalopathy (area under curve, AUC) . Results: There was no significant difference between the serum NSE level of the patients with subacute 1, 2-DCE poisoning in the onset group and the normal control group and the improvement group (P>0.05) . The serum NSE level of subacute 1, 2-DCE poisoning in the improvement group was lower than those in the normal control group (P<0.01) . The serum NSE level of the subacute 1, 2-DCE poisoning in the onset group was lower than those in the typical acute toxic encephalopathy in the onset group (P<0.01) . There was no linear correlation between cerebrospinal fluid NSE and serum NSE in patients with subacute 1, 2-DCE poisoning (r=-0.183, P=0.52) . ROC curve showed that the AUC of serum NSE in diagnosing subacute 1, 2-DCE poisoning and typical acute toxic encephalopathy were 0.661 and 0.726, respectively. Conclusion: There is no significant change in serum NSE in patients with subacute 1, 2-DCE poisoning.

Psychometric evaluation of the Taiwanese version of the functional assessment of cancer therapy: a questionnaire for patients with lung cancer.

OBJECTIVE: The Functional Assessment of Cancer Therapy-Lung (FACT-L) consists of the Functional Assessment of Cancer Therapy-General (FACT-G) and the Lung Cancer Subscale. The FACT-L is commonly used to measure quality of life in patients with lung cancer. This study evaluated the reliability and validity of the FACT-L in examining patients with lung cancer in Taiwan. DESIGN: This was a methodology study. SETTING: Patients with lung cancer at a regional hospital in Northern Taiwan. PARTICIPANTS: Patients who had received an early diagnosis of lung cancer between 2013 and 2015 were recruited as respondents. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): To verify the reliability and validity of the Taiwanese version of the FACT-L. RESULTS: A total of 104 patients who had received an initial diagnosis of lung cancer were recruited. The overall internal consistency of the FACT-L, as assessed using Cronbach's α, was 0.82. Among the patients, 64 had a test-retest reliability (r) of 0.45 (P < 0.001) at 6 weeks after treatment. Moreover, longitudinal research indicated that the FACT-L detected score differences before and after treatment in these patients (Cohen's d = -0.26). The Taiwanese version of the FACT-L considers 2-year survival as the gold standard, and the optimal combination of sensitivity and specificity was obtained when the receiver operating characteristic curve revealed cutoff points of 80 and 68 for the FACT-L and FACT-G, respectively. CONCLUSIONS: The Taiwanese version of the FACT-L can be widely applied to assess the quality of life of patients with lung cancer.

[Predictive value of G-FAST score for acute anterior circulation stroke patients with large artery occlusion].

Objective: To investigate the accuracy of Gaze-face-arm-speech-time (G-FAST) score in the early diagnosis of acute anterior circulation stroke patients with large artery occlusion. Methods: A retrospective study was conducted to investigate the anterior circulation infarction (ACI) cases with complete vascular imaging data within 6 hours of onset in the Department of Neurology of Beijing Shijitan Hospital, Capital Medical University from May 2010 to April 2018. According to the results of digital subtraction angiography (DSA) or computed tomography angiography (CTA), the patients were divided into two groups: large artery occlusion group and non-large artery occlusion group. Accuracy of G-FAST score in predicting acute large artery occlusive stroke was analyzed by area under receiver operating characteristic curve (AUC). The predictive value of G-FAST score, National Institutes of Health Stroke Scale (NIHSS) score and Alberta stroke early CT score (ASPECTS) in predicting large artery occlusion was compared. Results: A total of 138 patients with acute anterior circulation ischemic stroke were included in the study, and 82 of them had large artery occlusion (59.4%). Univariate analysis showed that baseline NIHSS score (12.0 vs 8.9, P=0.000) and G-FAST score (3.1 vs 2.2, P=0.000) were significantly higher in patients with large artery occlusion than those without large artery occlusion, and ASPECTS was significantly lower than patients without large artery occlusion (7.4 vs 8.2, P=0.001). The results from ROC showed that G-FAST, NIHSS and ASPECTS were with the AUC of 0.781, 0.733 and 0.664, respectively. G-FAST score had the highest accuracy in predicting the anterior circulation arterial occlusion. The optimal threshold of G-FAST score was 2.5, with a sensitivity of 79.3% and a specificity of 64.3%. Further univariate analysis showed that percentage of large anterior vessel occlusion (LAVO) in G-FAST (≥3) group was significantly different from that in G-FAST (≤ 2) group [76.5%(65/85)∶32.1%(17/53), P=0.000]. Conclusions: G-FAST score has predictive value for acute anterior circulation arterial occlusive stroke. Endovascular treatment may need to consider in patients with high G-FAST score as early as possible when conditions permit.

Neck circumference may be a valuable tool for screening individuals with obesity: findings from a young Chinese population and a meta-analysis.

BACKGROUND: Central obesity and overweight/obesity can result in various chronic non-communicable diseases, such as cardiovascular disease, metabolic syndrome, and diabetes mellitus. Waist circumference (WC) and body mass index (BMI) are widely used to measure obesity despite their limitations. For example, WC and BMI cannot be measured in pregnant women and subjects with abdominal ascites or masses. Therefore, this study aims to determine the efficacy of neck circumference (NC) as a tool for screening central obesity and overweight/obesity. METHODS: A total of 1169 undergraduates aged 18-25 years were studied by a cross-sectional survey in China, 2016. Questionnaires and physical examinations were used to collect data. Receiver operator characteristic (ROC) curve was performed to determine the best threshold of NC for screening central obesity and overweight/obesity. Meanwhile, a meta-analysis was conducted to estimate the efficacy of NC for screening central obesity and overweight/obesity synthetically. RESULTS: NC was moderately correlated with WC and BMI. The ROC analysis showed that 37.1 cm for male and 32.6 cm for female were the best thresholds for central obesity, and 37.4 cm and 32.2 cm for overweight/obesity, respectively. The sensitivity, specificity, area under receiver operating curve (AUC) of central obesity and overweight/obesity were higher. In the meta-analysis, the pooled sensitivity, specificity, AUC and their 95%CI of NC for screening central obesity were 0.72 (0.68~ 0.75), 0.87 (0.74~ 0.94), 0.77 (0.73~ 0.80) for male and 0.73 (0.65~ 0.80), 0.80 (0.71~ 0.86), 0.82 (0.79~ 0.86) for female. For overweight/obesity, the pooled sensitivity, specificity, AUC and corresponding 95%CI were 0.83 (0.70~ 0.91), 0.77 (0.66~ 0.85), 0.86 (0.83~ 0.89) for male and 0.82 (0.71~ 0.90), 0.84 (0.61~ 0.95), 0.89 (0.86~ 0.92) for female. CONCLUSION: NC may not be a good tool for screening individuals with central obesity. But it may be a simple and valuable tool for screening individuals with overweight/obesity, especially in females.

A Novel Scoring System Predicting Aneurysm Incomplete Occlusion After Flow Diversion: A 10-Year Experience.

BACKGROUND: Factors impacting the rate of aneurysm occlusion after flow diversion (FD) have been well described in the literature. In this article, we sought to evaluate those variables to develop and validate a scoring system predicting aneurysm incomplete occlusion after FD. METHODS: Retrospective review of patients with intracranial aneurysms treated with FD at a single institution between March 2013 and March 2023. Multivariable logistic regression model was developed using factors associated with aneurysm incomplete occlusion. The ABC scoring system consisted of: Age (<60 years old: 0, 60-69 years: 1, 70-79: 2, and ≥80: 3), Branch coming out of the aneurysm dome/neck (yes: 2, no: 0), and Cigarette smoking history (never smoker: 1, current or past smoker: 0). The scoring system performance was evaluated with receiver operating characteristic curve and calculating the area under the curve. RESULTS: A total of 449 patients with 563 aneurysms treated in 482 procedures were evaluated. Most cases were females (81.7%) with a median age of 59 years old. At a median follow-up of 13.2 months, 84.0% of aneurysms were completely or near-complete occluded. The scoring system had an area under the curve of 0.71. A value ≥ 2, reached a sensitivity of 74.4%, a specificity of 60.9%, a likelihood ratio+ of 1.90, and proved to be reliable in predicting the risk of incomplete occlusion (odds ratio = 4.53; 95% confidence interval: 2.73-7.54; P < 0.001). CONCLUSIONS: The proposed ABC scoring system can be used to evaluate the risk of aneurysm incomplete occlusion after treatment with FD, identifying patients who would benefit from adjunctive coiling or alternative treatment modalities.

ROC curve analysis: a useful statistic multi-tool in the research of nephrology.

In the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis. Before implementing a biomarker in everyday clinical practice for screening a specific disease context, specific statistic measures are necessary to evaluate the diagnostic accuracy and performance of this biomarker. Receiver Operating Characteristic (ROC) Curve analysis is an important statistical method used to estimate the discriminatory performance of a novel diagnostic test, identify the optimal cut-off value for a test that maximizes sensitivity and specificity, and evaluate the predictive value of a certain biomarker or risk, prediction score. Herein, through practical examples, we aim to present a simple methodological approach to explain in detail the principles and applications of ROC curve analysis in the field of nephrology pertaining diagnosis and prognosis.

Risk factors for adverse outcomes in children with diabetic ketoacidosis.

OBJECTIVE: Develop a multivariable model to identify children with diabetic ketoacidosis (DKA) and/or hyperglycemic hyperosmolar state (HHS) at increased risk of adverse outcomes, and apply it to analyze adverse outcomes during and after the COVID-19 pandemic. DESIGN: Retrospective review of clinical data from 4565 admissions (4284 with DKA alone, 31 [0.7%] only HHS, 250 [5.4%] hyperosmolar DKA) to a large academic children's hospital from January 2010-June 2023. 2010-2019 data (N=3004) were used as a training dataset, and 2020-2021 (N=903) and 2022-2023 (N=658) data for validation. Death or intensive care unit stays >48 hours comprised a composite "Adverse Outcome" group. Risks for this composite outcome were assessed using generalized estimating equations. RESULTS: There were 47 admissions with Adverse Outcomes (1.5%) in 2010-2019, 46 (5.0%) in 2020-2021, and 16 (2.4%) in 2022-2023. Eight patients died (0.18%). Maximum serum glucose, initial pH and diagnosis of type 2 diabetes most strongly predicted Adverse Outcomes. The proportion of patients with type 2 diabetes was highest in 2020-2021. A multivariable model incorporating these factors had excellent discrimination (area under receiver operator characteristic curve [AUC] of 0.948) for the composite outcome in the training dataset, and similar predictive power (AUC 0.960 and 0.873) in the 2020-2021 and 2022-2023 validation datasets, respectively. In the full dataset, AUC for death was 0.984. CONCLUSIONS: Type 2 diabetes and severity of initial hyperglycemia and acidosis are independent risk factors for Adverse Outcomes, and explain the higher frequency of Adverse Outcomes during the COVID-19 pandemic. Risks decreased in January 2022-June 2023.

Can we predict sleep-disordered breathing in pregnancy? The clinical utility of symptoms.

Sleep-disordered breathing (SDB) is reported commonly during pregnancy and is associated with an increased risk of adverse maternal and fetal outcomes, but the majority of these data are based upon self-report measures not validated for pregnancy. This study examined the predictive value of screening questionnaires for SDB administered at two time-points in pregnancy, and attempted to develop an 'optimized predictive model' for detecting SDB in pregnancy. A total of 380 women were recruited from an antenatal clinic in the second trimester of pregnancy. All participants completed the Berlin Questionnaire and the Multivariable Apnea Risk Index (MAP Index) at recruitment, with a subset of 43 women repeating the questionnaires at the time of polysomnography at 37 weeks' gestation. Fifteen of 43 (35%) women were confirmed to have a respiratory disturbance index (RDI) > 5 h(-1) . Prediction of an RDI > 5 h(-1) was most accurate during the second trimester for both the Berlin Questionnaire (sensitivity 0.93, specificity 0.50, positive predictive value 0.50 and negative predictive value 0.93), and the MAP Index [area under the receiver operating characteristic (ROC) curve of 0.768]. A stepwise selection model identified snoring volume, a body mass index (BMI)≥32 kg m(-2) and tiredness upon awakening as the strongest independent predictors of SDB during pregnancy; this model had an area under the ROC curve of 0.952. We conclude that existing clinical prediction models for SDB perform inadequately as a screening tool in pregnancy. The development of a highly predictive model from our data shows promise for a quick and easy screening tool to be validated for future use in pregnancy.

Refining the moose serum progesterone threshold to diagnose pregnancy.

Pregnancy determination is necessary for sound wildlife management and understanding population dynamics. Pregnancy rates are sensitive to environmental and physiological factors and may indicate the overall trajectory of a population. Pregnancy can be assessed through direct methods (rectal palpation, sonography) or indicated using hormonal assays (serum progesterone or pregnancy-specific protein B, fecal progestogen metabolites). A commonly used threshold of 2 ng/ml of progesterone in serum has been used by moose biologists to indicate pregnancy but has not been rigorously investigated. To refine this threshold, we examined the relationship between progesterone concentrations in serum samples and pregnancy in 87 moose (Alces alces; 64 female, 23 male) captured from 2010 to 2020 in the Grand Portage Indian Reservation in northeastern Minnesota, USA. Pregnancy was confirmed via rectal palpation (n = 25), necropsy (n = 2), calf observation (n = 25) or characteristic pre-calving behavior (n = 6), with a total of 58 females determined pregnant and 6 not pregnant; 23 males were included to increase the non-pregnant sample size. Using receiver operating characteristic analysis, we identified an optimal threshold of 1.115 ng/ml with a specificity of 0.97 (95% confidence interval [CI] = 0.90-1.00) and a sensitivity of 0.98 (95% CI = 0.95-1.00). Progesterone concentrations were significantly higher in cases of pregnant versus non-pregnant cows, but we did not detect a difference between single and twin births. We applied our newly refined threshold to calculate annual pregnancy rates for all female moose (n = 133) captured in Grand Portage from 2010 to 2021. Mean pregnancy rate during this period was 91% and ranged annually from 69.2 to 100%. Developing a reliable method for determining pregnancy status via serum progesterone analyses will allow wildlife managers to assess pregnancy rates of moose without devoting substantial time and resources to palpation and calf monitoring.

Clinical significance of serum CDC42 in the prediction of uremic vascular calcification incidence and progression.

Vascular calcification (VC) is prevalent in uremia patients, lacking effective molecular biomarkers. This study was conducted to explore the role of serum cell division cycle 42 (CDC42) in the diagnosis of uremic VC incidence and progression. We enrolled 104 uremia patients and selected arcus aortae calcification (AAC) as the outcome phenotype. Levels of CDC42, 1,25-dihydroxy vitamin D (1,25(OH) 2-D), fibroblast growth factor-23 (FGF-23), and other laboratory parameters in the blood were measured. The receiver operator characteristic curve, the Pearson test, and the multivariate Logistic regression were used for the analysis of CDC42 diagnostic values, correlation analysis, and screening of VC risk factors, respectively. CDC42 was higher in the serum of uremia patients with VC and elevated with the increase in AAC level. Serum CDC42 level>1.025 was predictive of VC incidence with 83.58% sensitivity and 56.76% specificity, and CDC42 level>1.280 was predictive of VC progression with 73.33% sensitivity and 68.18% specificity. Serum CDC42 was positively correlated with 1,25(OH) 2-D and FGF-23. Uremia patients with higher serum CDC42 had a higher probability of VC incidence and progression. Generally, serum CDC42 helped the diagnosis of uremic VC incidence and progression and was an independent risk factor for uremic VC progression.

Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.

Objective: To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging. Design: Evaluation of a diagnostic test or technology. Subjects: Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed. Methods: We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset. Main Outcome Measures: Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms. Results: Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task. Conclusions: Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning.

Severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor­Kappa B (NF­κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS­CoV­2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID­19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach.

The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases. Molecular-based methods such as the real-time reverse transcription polymerase chain reaction (rRT-PCR) for detecting COVID-19 is time consuming and prone to contamination. Modern bioinformatics tools have made it possible to create large databases of protein sequences of various diseases, apply data mining techniques, and accurately diagnose diseases. However, the current sequence alignment tools that use these databases are not able to detect novel COVID-19 viral sequences due to high sequence dissimilarity. The objective of this study, therefore, was to develop models that can accurately classify COVID-19 viral sequences rapidly using protein vectors generated by neural word embedding technique. Five machine learning models; K nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), Linear discriminant analysis (LDA), and Logistic regression were developed using datasets from the National Center for Biotechnology. Our results suggest, the RF model performed better than all other models on the training dataset with 99% accuracy score and 99.5% accuracy on the testing dataset. The implication of this study is that, rapid detection of the COVID-19 virus in suspected cases could potentially save lives as less time will be needed to ascertain the status of a patient.

Screening diagnostic markers for acute myeloid leukemia based on bioinformatics analysis.

Background: An in-depth understanding of the key molecules and associated mechanisms involved in acute myeloid leukemia (AML) carcinogenesis, proliferation, and relapse is critical. This provides a basis for disease screening, early diagnosis, and development of effective treatment strategies and prognosis. Methods: We downloaded AML transcription data sets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were screened by R software and limma packages. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on DEGs by public databases. In the DEG set, a random forest algorithm was used to identify characteristic genes of AML. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic efficacy of selected characteristic genes, which provided clues for the discovery of early diagnostic markers. The Estimate score was calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. Spearman's correlation test was used to explore the correlation between characteristic genes and Estimate Score, which provided clues for clarifying the potential pathogenic mechanism of key genes. Results: A total of 1,494 DEGs were identified from AML samples and normal samples, among which 1,181 genes were upregulated and 313 genes were downregulated in AML. There were 2 genes with a mean decrease Gini >2, namely, CDC20 and ESM1, respectively. The ROC curve showed that the area under the curve (AUC) of CDC20 was 0.966, and the 95% confidence interval (CI) was (0.939 to 0.987) (P<0.001). The AUC of ESM1 was 0.905, and 95% CI: 0.849 to 0.953 (P<0.001). Correlation analysis showed that CDC20 expression was negatively correlated with Estimate Score (R=-0.21, P=0.0036) in AML. The expression of ESM1 was negatively correlated with Estimate Score (R=-0.57, P<0.001). Conclusions: The genes CDC20 and ESM1 were identified as AML characteristic genes by random forest algorithm. Both CDC20 and ESM1 have good diagnostic efficacy for AML. They may play a carcinogenic role by promoting tumor cell proliferation and inhibiting immune cell chemotaxis, which are potential biological markers.