[Clinical observation on the treatment of ossification of the posterior longitudinal ligament of the cervical spine using 3D printed self-stable zero-profile artificial vertebral body]

Objective: To observe the clinical efficacy of a 3D printed self-stable zero-profile artificial vertebral body for anterior cervical corpectomy decompression and fusion in the treatment of ossification of the posterior longitudinal ligament (OPLL) of the cervical spine. Methods: In this prospective randomized controlled trial, patients diagnosed with OPLL in Luohe Central Hospital from January to July 2022 were divided into a zero-profile group (3D printed self-stable zero-profile artificial vertebral body was used for internal fixation and fusion after anterior cervical subtotal decompression,) and titanium-mesh group (titanium-mesh and titanium plate were used for internal fixation and fusion after anterior cervical subtotal decompression) according to envelope random method. Operation time, intraoperative blood loss, Japanese Orthopaedic Association (JOA) score and improvement rate, incidence of postoperative prosthesis subsidence, and bone graft fusion were recorded and compared between the two groups. Results: Finally, 21 patients in the zero notch group and 20 patients in the titanium mesh group were included in the study and were followed-up. In the zero-profile group, there were 16 males and 5 females, aged (48.0±12.7) years. In the titanium-mesh group, there were 14 males and 6 females, aged (49.8±10.2) years. All the 41 patients successfully completed the operation. In the zero-profile group, the surgical time was (50.04±8.45) minutes, the blood loss was (95.38±26.07) ml and the hospitalization cost was (42.32±6.12) thousand yuan. In the titanium-mesh group, the surgical time was (59.20±11.95) minutes, the blood loss was (93.10±27.86) ml and the hospitalization cost was (42.10±6.71) thousand yuan. The surgical time in the zero-profile group was shorter than that in the titanium-mesh group (P=0.007), and there was no statistically significant difference in blood loss and hospitalization costs between the two groups (both P>0.05). The 41 patients were followed-up for (14.29±1.45) months. Four cases (20.0%) in the titanium mesh group experienced swallowing difficulties and 0 cases in the zero incision group, the difference between the two groups was statistically significant (P=0.048). No intraoperative hematoma, spinal cord nerve recompression, airway crisis, incision infection complications, and no steel plate or screw breakage or displacement occurred after surgery. At the last follow-up, all cases had bone fusion. At the follow-up of 12 months after surgery, the JOA score of the zero incision group increased from preoperative (10.33±1.71) points to (15.47±0.81) points, with an improvement rate of 76.1%±15.7%; the JOA score of the titanium mesh group increased from (10.30±1.75) points to (15.30±0.92) points, with an improvement rate of 73.2%±16.7%; there was no statistically significant difference in improvement rate between the two groups (P=0.580). At the follow-up of 12 months after surgery, 1 case (4.8%) in the zero incision group and 8 cases (40.0%) in the titanium mesh group experienced implant sinking, and the difference between the two groups was statistically significant (P=0.009). Conclusion: Compared with titanium-mesh, 3D printed self-stable zero-profile artificial vertebral body for the treatment of OPLL of the cervical spine can achieve good surgical efficacy, shorter surgical time, lower incidence of postoperative chronic swallowing discomfort, and can provide a better bone material bonding interface and be less prone to prosthesis settlement.

Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis.

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the "high exertion" or "low exertion" class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

[Efficacy and safety of chimeric antigen receptor T-cell therapy followed by allogeneic hematopoietic stem cell transplantation in 21 patients with Ph-like acute lymphoblastic leukemia]

Objective: To evaluate the efficacy and safety of chimeric antigen receptor T-cell (CAR-T) therapy followed by allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with Ph-like acute lymphoblastic leukemia (Ph-ALL) . Methods: Patients with Ph-ALL who underwent CAR-T therapy followed by allo-HSCT from March 2018 to August 2023 at the First Affiliated Hospital of Soochow University were included, and their clinical data were retrospectively analyzed. Results: Of the 21 patients, 14 were male and 7 were female. The median age at the time of CAR-T therapy was 22 (6-50) years. Seven patients had ABL1-like rearrangements, and 14 had JAK-STAT rearrangements. Prior to CAR-T therapy, 12 patients experienced hematologic relapse; 7 were multiparameter flow cytometry minimal residual disease (MFC-MRD) -positive and 2 were MFC-MRD-negative. CAR-T cells were derived from patients' autologous lymphocytes. Nine patients were treated with CD19 CAR-T cells, and 12 were treated with CD19/CD22 CAR-T cells. After assessment on day 28 after CAR-T therapy, 95.2% of the patients achieved complete remission, with an MRD-negative remission rate of 75%. Nineteen patients developed grade 0-2 cytokine release syndrome (CRS) and 2 patients suffered grade 3 CRS, all cases of which resolved after treatment. All patients underwent allo-HSCT after CAR-T therapy. The median time from CAR-T therapy to allo-HSCT was 63 (38-114) days. Five patients experienced relapse after CAR-T therapy, including four with hematologic relapse and one with molecular relapse. The 3-year overall survival (OS) rates in the ABL1 and JAK-STAT groups were (83.3±15.2) % and (66.6±17.2) %, respectively (P=0.68) . The 3-year relapse-free survival (RFS) rates were (50.0±20.4) % and (55.6±15.4) % in the ABL1 and JAK-STAT groups, respectively. There was no significant difference in 3-year OS or RFS between the two groups. Conclusions: CAR-T therapy followed by allo-HSCT leads to rapid remission in most patients with Ph-ALL and prolongs leukemia-free survival.

Exercise Exertion Level Prediction Using Data from Wearable Physiologic Monitors

This study aims to develop machine learning (ML) algorithms to predict exercise exertion levels using physiological parameters collected from wearable devices. Real-time ECG, oxygen saturation, pulse rate, and revolutions per minute (RPM) data were collected at three intensity levels during a 16-minute cycling exercise. Parallel to this, throughout each exercise session, the study subjects' ratings of perceived exertion (RPE) were gathered once per minute. Each 16-minute exercise session was divided into a total of eight 2-minute windows. Each exercise window was labeled as "high exertion," or "low exertion" classes based on the self-reported RPEs. For each window, the gathered ECG data were used to derive the heart rate variability (HRV) features in the temporal and frequency domains. Additionally, each window's averaged RPMs, heart rate, and oxygen saturation levels were calculated to form all the predictive features. The minimum redundancy maximum relevance algorithm was used to choose the best predictive features. Top selected features were then used to assess the accuracy of ten ML classifiers to predict the next window's exertion level. The k-nearest neighbors (KNN) model showed the highest accuracy of 85.7% and the highest F1 score of 83%. An ensemble model showed the highest area under the curve (AUC) of 0.92. The suggested method can be used to automatically track perceived exercise exertion in real-time.

Quality of reporting of integrative Chinese and Western medicine intervention in randomized controlled trials of ulcerative colitis: a review

BACKGROUND

Integrative Chinese and Western medicine (ICWM) is commonly used for the treatment of ulcerative colitis (UC) in clinical practice. However, it is unclear whether the details of ICWM interventions, such as selection rationale, implementation design, and potential interactions, were adequately reported. Therefore, this study aimed to assess the quality of reporting in the ICWM interventional randomized controlled trials (RCTs) of UC and to identify the common problems if any.

METHODS

Through a search of 10 international electronic databases, we identified RCTs of UC with ICWM interventions published in English or Chinese from the inception date of each database up to 16 June 2023. Literature screening was strictly conducted based on the inclusion and exclusion criteria of the Population, Concept, and Context (PCC) framework. The general characteristics of the included studies were described. The quality of reporting was assessed according to three checklists, including the CONSORT (Consolidated Standards of Reporting Trials) with 36 items (except for one item 1b about abstract), the CONSORT for Abstracts (17 items), and a self-designed ICWM-related checklist (27 items covering design rationale, intervention details, outcome assessments, and analysis). The reporting scores of RCTs published before and after 2010 were compared.

RESULTS

A total of 1458 eligible RCTs were included. For the reporting compliance, the median score (interquartile ranges) of the CONSORT (72 score in total), the CONSORT for Abstract (34 score), and ICWM-related (54 score) items was 21 (18-25), 13 (12-15), and 18 (15-21), respectively. Although the time period comparisons showed that reporting quality of included publications improved significantly after the CONSORT 2010 issued (P < 0.01), more than 50% of items were evaluated as poor quality (reporting rate < 65%) among each checklist, especially in the CONSORT for Abstract and ICWM-specific items.

CONCLUSION

Although CONSORT appears to have enhanced the reporting of RCTs in UC, the quality of ICWM specifics is variable and in need of improvement. Reporting guidelines of the ICWM recommendations should be developed to improve their quality.

Using Electronic Medical Records and Clinical Notes to Predict the Outcome of Opioid Treatment Program

Opioid addiction is a serious public health problem in the US, and this study aimed to explore how natural language processing (NLP) can be used to identify factors that contribute to distress in individuals with opioid addiction, and then use this information along with structured data to predict the outcome of opioid treatment programs (OTP). The study analyzed medical records data and clinical notes of 1,364 patients, out of which 136 succeeded in the program and 1,228 failed. The results showed that several factors influenced the success of patients in the program, including sex, race, education, employment, secondary substance, tobacco use, and type of residences. XGBoost with down sampling was the best model. The accuracy of the model was 0.71 and the AUC score was 0.64. The study highlights the importance of using both structured and unstructured data to evaluate the effectiveness of OTP.

Exercise Exertion Levels Prediction Based on Real-Time Wearable Physiological Signal Monitoring

The real-time revolutions per minute (RPM) data, ECG signal, pulse rate, and oxygen saturation levels were collected during 16-minute cycling exercises. In parallel, ratings of perceived exertion (RPE) were collected each minute from the study participants. A 2-minute moving window, with one minute shift, was applied to each 16-minute exercise session to divide it into a total of fifteen 2-minute windows. Based on the self-reported RPE, each exercise window was labeled as "high exertion" or "low exertion" classes. The heart rate variability (HRV) characteristics in time and frequency domains were extracted from the collected ECG signals for each window. In addition, collected oxygen saturation levels, pulse rate, and RPMs were averaged for each window. The best predictive features were then selected using the minimum redundancy maximum relevance (mRMR) algorithm. Top selected features were then used to assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes model demonstrated the best performance with an accuracy of 80% and an F1 score of 79%.

[Structural changes of the frontal cortex in depressed mice are associated with decreased expression of brain-derived neurotrophic factor]

OBJECTIVE

To investigate the changes in gray matter volume in depressive-like mice and explore the possible mechanism.

METHODS

Twenty-four 6-week-old C57 mice were randomized equally into control group and model group, and the mice in the model group were subjected to chronic unpredictable mild stimulation (CUMS) for 35 days. Magnetic resonance imaging was performed to examine structural changes of the grey matter volume in depressive-like mice. The expression of brain-derived neurotrophic factor (BDNF) in the grey matter of the mice was detected using Western blotting and immunofluorescence staining.

RESULTS

Compared with the control mice, the mice with CUMS showed significantly decreased central walking distance in the open field test (P < 0.05) and increased immobile time in forced swimming test (P < 0.05). Magnetic resonance imaging showed that the volume of the frontal cortex was significantly decreased in CUMS mice (P < 0.001, when the mass level was greater than or equal to 10 756, the FDRc was corrected with P=0.05). Western blotting showed that the expression of mature BDNF in the frontal cortex was significantly decreased in CUMS mice (P < 0.05), and its expression began to decrease after the exposure to CUMS as shown by immunofluorescence staining. The volume of different clusters obtained by voxel-based morphometry (VBM) analysis was correlated with the expression level of mature BDNF detected by Western blotting (P < 0.05).

CONCLUSION

The decrease of frontal cortex volume after CUMS is related with the reduction of mature BDNF expression in the frontal cortex.

[Advances in clinical application of electrical impedance tomography to evaluate pulmonary perfusion in critically ill patients]

Lung perfusion monitoring can provide accurate information on changes in pulmonary blood flow in critically ill patients, and thus help guide clinical diagnosis and treatment. However, due to inconveniences such as patient transport, conventional imaging techniques are unable to meet the demand for real-time monitoring of lung perfusion, more convenient and reliable real-time functional imaging techniques should be developed to optimise cardiopulmonary management in critically ill patients. Electrical impedance tomography (EIT) is a non-invasive, radiation-free, bedside functional imaging technique that can be used to assess lung perfusion in patients with acute respiratory distress syndrome, pulmonary embolisms and other conditions, thereby assisting with the diagnosis of disease, the adjustment of treatment protocols, and the assessment of treatment outcomes. In this review, we focused on advances in EIT for lung perfusion monitoring in critically ill patients.

Automated Classification of Exercise Exertion Levels Based on Real-Time Wearable Physiological Signal Monitoring

This study aimed to build machine learning (ML) algorithms for the automated classification of cycling exercise exertion levels using data from wearable devices. The best predictive features were selected using the minimum redundancy maximum relevance algorithm (mRMR). Top selected features were then used to build and assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes showed the best F1 score of 79%. The proposed approach may be used for real-time monitoring of exercise exertion.

[Analysis of the status of excess heart age and its risk factors among residents aged 35 to 64 years in China]

Objective: To analyze the status of excess heart age and its risk factors among Chinese residents aged 35 to 64 years. Methods: The study subjects were Chinese residents aged 35 to 64 years who completed the heart age assessment by WeChat official account"Heart Strengthening Action"through the internet from January 2018 to April 2021. Information such as age, gender, body mass index (BMI), blood pressure, total cholesterol (TC), smoking history, and diabetes history was collected. The heart age and excess heart age were calculated according to the characteristics of individual cardiovascular risk factors and the heart aging was defined as excess heart age≥5 years and 10 years respectively. The heart age and standardization rate were calculated respectively based on the population standardization of the 7th census in 2021.CA trend test was used to analyze the changing trend of excess heart age rate and population attributable risk (PAR) was used to calculate the contribution of risk factors. Results: The mean age of 429 047 subjects was 49.25±8.66 years. The male accounted for 51.17% (219 558/429 047) and the excess heart age was 7.00 (0.00, 11.00) years. The excess heart age rate defined by excess heart age≥5 years and≥10 years was 57.02% (the standardized rate was 56.83%) and 38.02% (the standardized rate was 37.88%) respectively. With the increase of the age and number of risk factors, the excess heart age rate of the two definitions showed an upward trend according to the result of the trend test analysis (P<0.001). The top two risk factors of the PAR for excess heart age were overweight or obese and smoking. Among them, the male was smoking and overweight or obese, while the female was overweight or obese and having hypercholesterolemia. Conclusion: The excess heart age rate is high in Chinese residents aged 35 to 64 years and the contribution of overweight or obese, smoking and having hypercholesterolemia ranks high.

Mechanism of Qingre Huoxue Fang treatment on inhibiting angiogenesis of rheumatoid arthritis based on network pharmacology and in vitro experiments

This study aimed to explore the mechanism of Qingre Huoxue Fang (QRHXF) treatment on anti-angiogenesis in rheumatoid arthritis (RA) based on network pharmacology and in vitro experiments. We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Therapeutic Target (TTD) database to extract the active components of QRHXF and potential targets for regulating angiogenesis. First, we used Cytoscape bioinformatics software to construct the network of QRHXF-angiogenesis and screened the potential targets. Then, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the potential core targets. In addition, enzyme-linked immune assay and Western blot were used for in vitro validation and to verify the effects of different concentrations of QRHXF on the expression levels of the vascular endothelial growth factor receptor type 1 (VEGFR-1) and VEGFR-2 cytokines and phosphoinositide 3-kinase (PI3k) and Ak strain transforming (Akt) proteins in human umbilical vein endothelial cells (HUVECs). In results, we screened 179 core QRHXF antiangiogenic targets, including vascular endothelial growth factor (VEGF) cytokines. Enrichment analysis showed that the targets were enriched in 56 core signaling pathways, including PI3k and Akt. In vitro experiments showed that the migration distance and square, adhesion optical density (OD) values, and the number of branch points in tube formation significantly decreased in the QRHXF group compared with the induced group (P<0.01). Notably, the serum levels of VEGFR-1 and VEGFR-2 were lower compared with the induced group (P<0.05 or P<0.01). In addition, the expressions of PI3K and p-Akt proteins were decreased in the middle- and high doses groups (P<0.01). This study's results suggest that the downstream mechanism of QRHXF anti-angiogenesis might inhibit the PI3K-Akt signalling pathway and downregulate VEGF-1 and VEGF-2.

Mechanism of Qingre Huoxue Fang treatment on inhibiting angiogenesis of rheumatoid arthritis based on network pharmacology and in vitro experiments

This study aimed to explore the mechanism of Qingre Huoxue Fang (QRHXF) treatment on anti-angiogenesis in rheumatoid arthritis (RA) based on network pharmacology and in vitro experiments. We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Therapeutic Target (TTD) database to extract the active components of QRHXF and potential targets for regulating angiogenesis. First, we used Cytoscape bioinformatics software to construct the network of QRHXF-angiogenesis and screened the potential targets. Then, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the potential core targets. In addition, enzyme-linked immune assay and Western blot were used for in vitro validation and to verify the effects of different concentrations of QRHXF on the expression levels of the vascular endothelial growth factor receptor type 1 (VEGFR-1) and VEGFR-2 cytokines and phosphoinositide 3-kinase (PI3k) and Ak strain transforming (Akt) proteins in human umbilical vein endothelial cells (HUVECs). In results, we screened 179 core QRHXF antiangiogenic targets, including vascular endothelial growth factor (VEGF) cytokines. Enrichment analysis showed that the targets were enriched in 56 core signaling pathways, including PI3k and Akt. In vitro experiments showed that the migration distance and square, adhesion optical density (OD) values, and the number of branch points in tube formation significantly decreased in the QRHXF group compared with the induced group (P<0.01). Notably, the serum levels of VEGFR-1 and VEGFR-2 were lower compared with the induced group (P<0.05 or P<0.01). In addition, the expressions of PI3K and p-Akt proteins were decreased in the middle- and high doses groups (P<0.01). This study's results suggest that the downstream mechanism of QRHXF anti-angiogenesis might inhibit the PI3K-Akt signalling pathway and downregulate VEGF-1 and VEGF-2.

Development and Evaluation of Wireless Interfaces to Monitor and Control Cycling Exercise During Home Telerehabilitation

Purpose

This paper focuses on developing and testing three versions of interactive bike (iBikE) interfaces for remote monitoring and control of cycling exercise sessions to promote upper and lower limb rehabilitation.

Methods

Two versions of the system, which consisted of a portable bike and a tablet PC, were designed to communicate through either Bluetooth low energy (BLE) or Wi-Fi interfaces for real-time monitoring of exercise progress by both the users and their clinical team. The third version of the iBikE system consisted of a motorized bike and a tablet PC. It utilized conventional Bluetooth to implement remote control of the motorized bike's speed during an exercise session as well as to provide real-time visualization of the exercise progress. We developed three customized tablet PC apps with similar user interfaces but different communication protocols for all the platforms to provide a graphical representation of exercise progress. The same microcontroller unit (MCU), ESP-32, was used in all the systems.

Results

Each system was tested in 1-minute exercise sessions at various speeds. To evaluate the accuracy of the measured data, in addition to reading speed values from the iBikE app, the cycling speed of the bikes was measured continuously using a tachometer. The mean differences of averaged RPMs for both data sets were calculated. The calculated values were 0.38 ± 0.03, 0.25 ± 0.27, and 6.7 ± 3.3 for the BLE system, the Wi-Fi system, and the conventional Bluetooth system, respectively.

Conclusion

All interfaces provided sufficient accuracy for use in telerehabilitation.

[Survival rate and quality of life of human papillomaviruse-negative patients with advanced oropharyngeal cancer receiving different treatments]

OBJECTIVE

To analyze the survival rates and quality of life of human papillomaviruse (HPV)-negative patients with advanced oropharyngeal cancer after different combined treatments with chemotherapy, surgery and radiotherapy.

METHODS

We retrospectively analyzed the data of patients with oropharyngeal cancer hospitalized in our hospital from January, 2015 to December, 2020, and after case analysis of the clinical, imaging and pathological data, 405 patients were included in this study and grouped according to the treatments they received. Kaplan-Meier and Log-rank analysis were used to calculate the overall survival rate and the survival rate of patients with different treatments. The self-rated quality of life of the tumor-free survivors was assessed using UW-QOL (4) questionnaire and compared among the patients with different treatments.

RESULTS

Among the 405 patients included in this study, 146 received treatments with chemotherapy+surgery+radiotherapy (CSRT), 138 received surgery+radiotherapy (SRT) and 121 were treated with radiotherapy+chemotherapy (RCT). The overall survival rates of the 3 groups at 1, 3 and 5 years were 85.1%, 67.1% and 56.9%, respectively, and the survival rates of patients receiving CSRT, SRT and RCT did not differ significantly (P > 0.05). A total of 280 UW-QOL (4) questionnaires were distributed and 202 (72.14%) were retrieved. The average total scores decreased in the order of CSRT > SRT > RCT; the scores were significantly higher in CSRT group than in SRT and RCT (P < 0.05), but did not differ significantly between SRT and RCT groups (P > 0.05).

CONCLUSION

CSRT, SRT and RCT are all treatment options for locally advanced oropharyngeal cancer, but CSRT may achieve better quality of life of the patients than SRT and RCT.

Genetic subtypes and phenotypic characteristics of 110 patients with Prader-Willi syndrome

BACKGROUND

Prader-Willi syndrome (PWS) is a complex disorder caused by impaired paternally expressed genes on chromosome 15q11-q13. Variable findings have been reported about the phenotypic differences among PWS genetic subtypes.

METHODS

A total of 110 PWS patients were diagnosed from 8,572 pediatric patients included from July 2013 to December 2021 by MLPA and MS-MLPA assays. Atypical deletions were defined by genomic CNV-sequencing. Maternal uniparental disomy (UPD) was subgrouped by microsatellite genotyping. Clinical data were collected for phenotype-genotype associations. Twenty-one patients received growth hormone (GH) treatment, and the anthropometric and laboratory parameters were evaluated and compared.

RESULTS

Genetically, the 110 patients with PWS included 29 type I deletion, 56 type II deletion, 6 atypical deletion, 11 heterodisomy UPD, and 8 isodisomy UPD. The UPD group had significantly higher maternal age (31.4 ± 3.4 vs 27.8 ± 3.8 years), more anxiety (64.29% vs 26.09%) and autistic traits (57.14% vs 26.09%), and less hypopigmentation (42.11% vs 68.24%) and skin picking (42.86% vs 71.01%) than the deletion group. The type I deletion group was diagnosed at earlier age (3.7 ± 3.3 vs 6.2 ± 3.2 years) and more common in speech delay (95.45% vs 63.83%) than the type II. The isodisomy UPD group showed a higher tendency of anxiety (83.33% vs 50%) than the heterodisomy. GH treatment for 1 year significantly improved the SDS of height (- 0.43 ± 0.68 vs - 1.32 ± 1.19) and IGF-I (- 0.45 ± 0.48 vs - 1.97 ± 1.12). No significant changes were found in thyroid function or glucose/lipid metabolism.

CONCLUSION

We explored the physical, psychological and behavioral phenotype-genotype associations as well as the GH treatment effect on PWS from a large cohort of Chinese pediatric patients. Our data might promote pediatricians' recognition and early diagnosis of PWS.

Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program

Potential of natural language processing (NLP) in extracting patient's information from clinical notes of opioid treatment programs (OTP) and leveraging it in development of predictive models has not been fully explored. The goal of this study was to assess potential of NLP in identifying legal, social, mental, medical and family environment-based determinants of distress from clinical narratives of patients with opioid addiction, and then using this information in predicting OTP outcomes. Around 63% of patients reported improvements after completing OTP. We compared the results of logistics regression and random forest for predictive modeling. Random forest model performed slightly better than logistic regression (75% F1 score) with 74% accuracy. Clinical Relevance- Psychiatric and medical disorders, social, legal and family-based distress are important determinants of distress in patients enrolled in OTP. These information are often recorded in clinical notes. Extraction of this information and their utilization as features in machine learning models will lead to the enhancement of the performance of the OTP outcome predictive models.

Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital

No-show visits are a serious problem for healthcare centers. It costs a major hospital over 15 million dollars annually. The goal of this paper was to build machine learning models to identify potential no-show telemedicine visits and to identify significant factors that affect no-show visits. 257,293 telemedicine sessions and 152,164 unique patients were identified in Mount Sinai Health System between March 2020 and December 2020. 5,124 (2%) of these sessions were no-show encounters. Extreme Gradient Boosting (XGB) with under-sampling was the best machine learning model to identify no-show visits using telemedicine service. The accuracy was 0.74, with an AUC score of 0.68. Patients with previous no-show encounters, non-White or non-Asian patients, and patients living in Bronx and Manhattan were all important factors for no-show encounters. Furthermore, providers' specialties in psychiatry and nutrition, and social workers were more susceptible to higher patient no-show rates.

Using EHR Data to Identify Social Determinants of Health Affecting Disparities in Cancer Survival

The aim of this pilot study was to identify social determinants of health (SDH) that affect disparities in cancer survival. A limited dataset was generated by querying electronic medical records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics that affected survival in 22,096 cancer patients were analyzed using descriptive statistics and logistic regression analyses. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Percentage of individuals with short survival in Blacks and Whites was respectively 41.4% and 22.2% for lung cancer, 9.8% and 7.1% for colorectal cancer, 2.9% and 0.7% for breast cancer, 6.8% and 4.0% for multiple myeloma, and 1.4% and 0.8% for prostate cancer. Logistic regression identified SDH factors increasing likelihood of shorter survival that included older age, and being male, Black or Hispanic. We concluded that further analysis of a broader spectrum of SDH factors is warranted.

Using EHR data to compare survival disparities in major cancer types

e18517

Background: The aim of our study is to provide a comparative overview of survival disparities in 5 major cancer types: breast cancer, colorectal cancer, lung cancer, multiple myeloma and prostate cancer, within one health system in New York City. Using this approach, we aim at initial assessment of potential drivers of disparities in cancer survival, especially for those who deceased in a short amount of time after diagnosis. Methods: A limited dataset was generated from electronic medical records from Mount Sinai Health System for the five cancer types from January 2003 to November 2020. We extracted 2 subsets from the original dataset: patients who survived less than 1 year between diagnoses and death, and patients who have survived for over 5 years after diagnoses. We performed logistic regression to investigate the effect of demographical factors on patients’ duration of survival after cancer diagnosis. Results: There are 1280 patients who survived less than 1 year and 20,816 patients who survived over 5 years. Although there are less overall colorectal cancer and lung cancer diagnoses, there are significantly more patients who deceased within a short of period of time from these 2 cancers, comparing to the other 3 cancer types. Race was an important factor. There was significantly more proportions of black patients deceased in a short period of time than in a long time after diagnosis. Percentage of individuals with short survival in Blacks and Whites was respectively 41.4% and 22.2% for lung cancer, 9.8% and 7.1% for colorectal cancer, 2.9% and 0.7% for breast cancer, 6.8% and 4.0% for multiple myeloma, and 1.4% and 0.8% for prostate cancer. This disparity was observed in all five cancer types. When comparing within race, there were significantly higher proportions of black patients survived a short time than those survived a long time for all 5 cancer types. When comparing with other races, black patients who were diagnosed with lung cancer or breast cancer were more likely to be deceased in a short time after diagnosis compare to white patients. And Hispanic patients with lung cancer were also at risk of shorter survival length compared to white patients. Thus, promoting routine cancer screening is important in black and Hispanic communities. Conclusions: Logistic regression identified factors increasing likelihood of shorter survival that included older age, and being male, Black or Hispanic. We concluded that further analysis of broader spectrum of risk factors is warranted.

Identifying Determinants of Disparities in Lung Cancer Survival Rates from Electronic Health Record Data

The goal of this pilot study was to identify significant factors that affect disparities in lung cancer survival. A de-identified dataset was generated by querying electronic health records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics, cancer stage, and genetic profile were analyzed using logistic regression. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Male, Black and Hispanic patients and those who were diagnosed in later stages were the people most susceptible to a shorter length of survival after cancer diagnoses. In addition, we identified three genetic oncodrivers (KRAS, EGFR and TP53) which were highly correlated with the length of survival after lung cancer diagnoses and their distribution was associated with race. We concluded that EHR data provide important insights on cancer survival disparities.

Autophagy-Sirtuin1(SIRT1) Alleviated the Coronary Atherosclerosis (AS)in Mice through Regulating the Proliferation and Migration of Endothelial Progenitor Cells (EPCs) via wnt/β-catenin/GSK3β Signaling Pathway

BACKGROUND AND PURPOSE

SIRT1 was associated with AS risk and EPCs were reported to participate in the endothelial repair in Coronary Atherosclerosis (CAS). In this study, we explored the role of SIRT1 in AS mice and also its modulation in EPCs.

METHODS AND MATERIALS

ApoE-/-mice were fed on high-fat and high-glucose diet to establish the AS animal model with the normally-raised C57BL/6 mice as a control group. SIRT1 activator, SRT 2104 was injected intravenously into 5 ApoE-/-mice and its inhibitor Nicotinamide was injected in tail in another 5 ApoE-/-mice. Weight changes were recorded. Blood samples were taken from posterior orbital venous plexus and were detected by automatic biochemical analyzer. HE staining displayed the pathological conditions while Immunohistochemistry (IHC) evaluated the CD34+/VEGFR2+ relative density in the aorta tissues. EPCs were isolated from bone marrow and verified using immunofluorescence staining (IFS). The modulatory mechanism of SIRT1 in EPCs were studied by using RT-PCR, MTT, Western Blot and colony formation, scratch methods.

RESULTS

SIRT1 activator negatively regulated the weight and TC, TG and LDL levels, alleviated the lesion conditions and decreased the CD34+/VEGFR2+ density compared to the AS control. In vitro, SIRT1 activator promoted the proliferation and migration of EPCs and activated wnt/β-catenin/GSK3β signaling pathway. SIRT1 activator also inhibited the autophagy biomarkers ATG1 and LC3II. Furthermore, inhibitor of autophagy promoted SIRT1 expression and induced EPC proliferation, migration and activated wnt/β-catenin/GSK3β pathway. The suppression of the wnt/β-catenin/GSK3β pathway inhibited SIRT1 expression in EPCs, attenuated the proliferation and migration and promoted autophagy of EPCs.

CONCLUSION

SIRT1 activation might be protective in AS mice through autophagy inhibition in EPCs via wnt/β-catenin/GSK3β signaling pathway.

Comparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing

Psychiatric and medical disorders, social and family environment, and legal distress are important determinants of distress that impact the effectiveness of the treatment in opioid treatment program (OTP). This information is not routinely captured in electronic health record, but may be found in clinical notes. This study aims to explore the feasibility and effectiveness of natural language processing (NLP) strategy for identifying legal, social, mental and medical determinates of distress along with emotional pain rooted in family environment from clinical narratives of patients with opioid addiction, and then using this information to find its impact on OTP outcomes. Analysis in this study showed that mental and legal distress significantly impact the result of the treatment in OTP.

The Three Hundred–NIKA2 Sunyaev–Zeldovich Large Program twin samples: Synthetic clusters to support real observations

The simulation database of The Three Hundred Project has been used to pick synthetic clusters of galaxies with properties close to the observational targets of the NIKA2 camera Sunyaev–Zeldovich (SZ) Large Program. Cross–matching of cluster parameters such as mass and redshift of the cluster in the two databases has been implemented to generate the so–called twin samples for the Large Program. This SZ Large Program is observing a selection of galaxy clusters at intermediate and high redshift (0:5 < z < 0:9), covering one order of magnitude in mass. These are SZ–selected clusters from the Planck and Atacama Cosmology Telescope catalogs, wherein the selection is based on their integrated Compton parameter values, Y500: the value of the parameter within the characteristics radius R500.

The Three Hundred hydrodynamical simulations provide us with hundreds of clusters satisfying these redshift, mass, and Y500 requirements. In addition to the standard post-processing analysis of the simulation, mock observational maps are available mimicking X–ray, optical, gravitational lensing, radio, and SZ observations of galaxy clusters. The primary goal of employing the twin samples is to compare different cluster mass proxies from synthetic X–ray, SZ effect and optical maps (via the velocity dispersion of member galaxies and lensing κ-maps) of the clusters. Eventually, scaling laws between different mass proxies and the cluster mass will be cross–correlated to reduce the scatter on the inferred mass and the mass bias will be related to various physical parameters.

The Three Hundred project: Contrasting clusters galaxy density in hydrodynamical and dark matter only simulations

Cluster number count is a major cosmological probe for the next generation of cosmological large scale-structure surveys like the one expected from the Euclid satellite mission. Cosmological constraints will be mainly limited by the understanding of the selection function (SF), which characterize the probability of detecting a cluster of a given mass and redshift. The SF can be estimated by injecting realistic simulated clusters into the survey and re-applying the detection procedure. For this purpose we intend to use The Three Hundreds project, a 324 cluster sample simulated with full-physics hydrodynamical re-simulations. In this paper we concentrate on the study of the distribution of member galaxies in the cluster sample. First, we study possible resolution effects by comparing low and high resolution simulations. Finally, accounting for the latter we derive the density profiles of the member galaxies and discuss their evolution with cluster mass and redshift.

Use of Artificial Intelligence for Predicting COVID-19 Outcomes: A Scoping Review

During the COVID-19 pandemic, artificial intelligence has played an essential role in healthcare analytics. Scoping reviews have been shown to be instrumental for analyzing recent trends in specific research areas. This paper aimed at applying the scoping review methodology to analyze the papers that used artificial intelligence (AI) models to forecast COVID-19 outcomes. From the initial 1,057 articles on COVID-19, 19 articles satisfied inclusion/exclusion criteria. We found that the tree-based models were the most frequently used for extracting information from COVID-19 datasets. 25% of the papers used time series to transform and analyze their data. The largest number of articles were from the United States and China. The reviewed artificial intelligence methods were able to predict cases, death, mortality, and severity. AI tools can serve as powerful means for building predictive analytics during pandemics.

Latent COVID-19 Clusters in Patients with Opioid Misuse

The goal of this paper is to apply unsupervised machine learning techniques in order to discover latent clusters in patients who have opioid misuse and also undergone COVID-19 testing. Target dataset has been constructed based on COVID-19 testing results at Mount Sinai Health System and opioid treatment program (OTP) information from New York State Office of Addiction Service and Support (OASAS). The dataset was preprocessed using factor analysis for mixed data (FAMD) method and then K-means algorithm along with elbow method were used to determine the number of optimal clusters. Four patient clusters were identified among which the fourth cluster constituted the maximum percentage of positive COVID-19 test results (20%). Compared to the other clusters, this cluster has the highest percentage of African Americans. This cluster has also the highest mortality rate (16.52%), hospitalization rate after receiving the COVID-19 test result (72.17%, use of ventilator (7.83%) and ICU admission rate (47.83%). In addition, this cluster has the highest percentage of patients with at least one chronic disease (99.13%) and age-adjusted comorbidity score more than 1 (83.48%). Longer participation in OTP was associated with the highest morbidity and mortality from COVID-19.

Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns

The goal of this study was to build a machine learning model for early prostate cancer prediction based on healthcare utilization patterns. We examined the frequency and pattern changes of healthcare utilization in 2916 prostate cancer patients 3 years prior to their prostate cancer diagnoses and explored several supervised machine learning techniques to predict possible prostate cancer diagnosis. Analysis of patients' medical activities between 1 year and 2 years prior to their prostate cancer diagnoses using XGBoost model provided the best prediction accuracy with high F1 score (0.9) and AUC score (0.73). These pilot results indicated that application of machine learning to healthcare utilization patterns may result in early identification of prostate cancer diagnosis.

Molecular and phenotypic characteristics of 15q24 microdeletion in pediatric patients with developmental disorders

Chromosome 15q24 microdeletion is a rare genetic disorder characterized by development delay, facial dysmorphism, congenital malformations, and occasional autism spectrum disorder (ASD). In this study, we identified five cases of 15q24 microdeletion using multiplex ligation-dependent probe amplification (MLPA) technology in a cohort of patients with developmental delay and/or intellectual disability. Two of these five cases had deletions that overlapped with the previously defined 1.1 Mb region observed in most reported cases. Two cases had smaller deletions (< 0.57 Mb) in the 15q24.1 low copy repeat (LCR) B-C region. They presented significant neurobehavioral features, suggesting that this smaller interval is critical for core phenotypes of 15q24 microdeletion syndrome. One case had minimal homozygous deletion of less than 0.11 Mb in the 15q24.1 LCR B-C region, which contained CYP1A1 (cytochrome P450 family 1 subfamily A member 1) and EDC3 (enhancer of mRNA decapping 3) genes, resulting in poor immunity, severe laryngeal stridor, and lower limbs swelling. This study provides additional evidence of 15q24 microdeletion syndrome with genetic and clinical findings. The results will be of significance to pediatricians in their daily practice.

[Identification of serum peptide biomarker for ovarian cancer diagnosis by Clin-TOF-II-MS combined with magnetic beads technology]

Objective: To explore the serum cyclic polypeptide biomarkers for ovarian cancer diagnosis. Methods: A total of 54 patients with epithelial ovarian cancer confirmed by pathology in Cancer Hospital, Chinese Academy of Medical Sciences from March 2018 to September 2018 were selected as the study subjects, and 40 healthy women with normal examination results in the cancer screening center were selected as the control. All of the samples were randomly divided into training set and validation set at the ratio of 1∶1 with a random number. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with magnetic bead technology was used for detecting peptide profiling in serum samples to screen significantly differently expressed peptides between ovarian cancer group and control group of the training set (score>5). Receiver operating characteristic (ROC) curve analysis was used to screen differential peptide peaks with area under curve (AUC) ≥0.8, sensitivity and specificity>90% in the training set and validation set. Liquid chromatography-mass spectrometry (LC-MS/MS) was further used to determine the composition of differentially expressed peptides. Results: By comparing the peptide profiles of the two groups, 102 differential peptide peaks were initially detected in the mass-to-charge ratio range of 1 000 to 10 000. ROC curve analysis showed that there were 42 differential peptide peaks with AUC ≥0.8 in both training set and validation set, 19 of which were highly expressed in ovarian cancer group, and 23 were lowly expressed. There were 15 different peptide peaks in highly expressed ovarian cancer group with sensitivity and specificity over 90%. The mass-to-charge ratios were 7 744.27, 5 913.41, 5 329.87, 4 634.21, 4 202.02, 3 879.26, 3 273.35, 3 253.79, 3 234.34, 2 950.33, 2 664.51, 2 018.38, 1 893.37, 1 498.69 and 1 287.55. There were 15 different peptide peaks in lowly expressed ovarian cancer group with sensitivity and specificity over 90%, the mass-to-charge ratios were 9 288.46, 7 759.77, 5 925.24, 4 652.77, 4 210.42, 3 887.02, 3 279.90, 3 240.82, 2 962.15, 2 932.70, 2 022.42, 1 897.16, 1 501.69, 1 337.38 and 1 290.13. No protein composition was identified in 15 different peptide peaks in lowly expressed ovarian cancer group. The two protein compositions identified in 15 different peptide peaks in highly expressed ovarian cancer group were recombinant serglycin (SRGN) and fibinogen alpha chain (FGA), the mass-to-charge ratios of which were 1 498.696 and 5 913.417, respectively. The sensitivity and specificity of the two proteins for ovarian cancer diagnosis were 100%, 100% and 90.9%, 100%, respectively. Conclusion: SRGN and FGA are highly expressed in the serum of ovarian cancer patients, which may be potential diagnostic markers for ovarian cancer.

[Association of GMF-β expression with Ki-67 and its significance in the prognostic evaluation of astrocytoma]

Objective: To clarify the correlation of the expression of glia maturation factor-β (GMF-β) with Ki-67 in astrocytoma, and to investigate the prognostic implications of combined detection of GMF-β and Ki-67. Methods: One hundred and forty human astrocytoma samples (WHO Ⅱ-Ⅳ grade) were collected at Southwest Hospital, Army Medical University (the Third Military Medical University), China from 2006 to 2009. Clinicopathological information and 3-year follow-up data were collected. Expression of GMF-β and Ki-67 was detected by single and double immunohistochemical staining, then the association of GMF-β expression with Ki-67 and its significance in prognostic evaluation of astrocytoma were statistically analyzed. Results: GMF-β expression in astrocytoma cells was correlated to both tumor grade and Ki-67 (both P<0.05); Kaplan-Meier survival analysis showed that GMF-β and Ki-67 expression were negatively correlated to the 3 year-survival rates, respectively (both P<0.01). Further analysis demonstrated that the two factors were co-influenced on survival, showing a trend of "GMF-β^low Ki-67^low>GMF-β^high Ki-67^low>GMF-β^low Ki-67^high>GMF-β^high Ki-67^high" in 3-year survival rate with significant intergroup differences (P<0.05, P<0.01). Conclusions: GMF-β expression is positively associated with Ki-67 in astrocytoma. Combined detection of GMF-β and Ki-67 can predict prognosis of patients with glioma.

Comparison of ACM and CLAMP for Entity Extraction in Clinical Notes

Rapid increase in adoption of electronic health records in health care institutions has motivated the use of entity extraction tools to extract meaningful information from clinical notes with unstructured and narrative style. This paper investigates the performance of two such tools in automatic entity extraction. In specific, this work focuses on automatic medication extraction performance of Amazon Comprehend Medical (ACM) and Clinical Language Annotation, Modeling and Processing (CLAMP) toolkit using 2014 i2b2 NLP challenge dataset and its annotated medical entities. Recall, precision and F-score are used to evaluate the performance of the tools.Clinical Relevance- Majority of data in electronic health records (EHRs) are in the form of free text that features a gold mine of patient's information. While computerized applications in healthcare institutions as well as clinical research leverage structured data. As a result, information hidden in clinical free texts needs to be extracted and formatted as a structured data. This paper evaluates the performance of ACM and CLAMP in automatic entity extraction. The evaluation results show that CLAMP achieves an F-score of 91%, in comparison to an 87% F-score by ACM.

Cell-free DNA from bile outperformed plasma as a potential alternative to tissue biopsy in biliary tract cancer

BACKGROUND

Biliary tract cancers (BTCs) are rare and highly heterogenous malignant neoplasms. Because obtaining BTC tissues is challenging, the purpose of this study was to explore the potential roles of bile as a liquid biopsy medium in patients with BTC.

PATIENTS AND METHODS

Sixty-nine consecutive patients with suspected BTC were prospectively enrolled in this study. Capture-based targeted sequencing was performed on tumor tissues, whole blood cells, plasma, and bile samples using a large panel consisting of 520 cancer-related genes.

RESULTS

Of the 28 patients enrolled in this cohort, tumor tissues were available in eight patients, and plasma and bile were available in 28 patients. Somatic mutations were detected in 100% (8/8), 71.4% (20/28), and 53.6% (15/28) of samples comprising tumor tissue DNA, bile cell-free DNA (cfDNA), and plasma cfDNA, respectively. Bile cfDNA showed a significantly higher maximum allele frequency than plasma cfDNA (P = 0.0032). There were 56.2% of somatic single-nucleotide variant (SNVs)/insertions and deletions (indels) shared between bile and plasma cfDNA. When considering the genetic profiles of tumor tissues as the gold standard, the by-variant sensitivity and positive predictive value for SNVs/indels in bile cfDNA positive for somatic mutations were both 95.5%. The overall concordance for SNVs/indels in bile was significantly higher than that in plasma (99.1% versus 78.3%, P < 0.0001). Moreover, the sensitivity of CA 19-9 combined with bile cfDNA achieved 96.4% in BTC diagnosis.

CONCLUSION

We demonstrated that bile cfDNA was superior to plasma cfDNA in the detection of tumor-related genomic alterations. Bile cfDNA as a minimally invasive liquid biopsy medium might be a supplemental approach to confirm BTC diagnosis.

[Predicting postoperative recurrence of stage Ⅰ-Ⅲ renal clear cell carcinoma based on preoperative CT radiomics feature nomogram]

OBJECTIVE

To explore the preoperative radiomics features (RFs) and construct a nomogram for predicting postoperative recurrence of stage Ⅰ-Ⅲ clear cell renal carcinoma (ccRCC).

METHODS

The clinicopathological data and preoperative enhanced CT images collected from 256 patients with ccRCC were used as the training dataset (175 patients) and test dataset (81 patients). The enhanced CT images of the tumor were segmented using ITK-SNAP software, and the RFs were extracted using the PyRadiomics computing platform. In the training dataset, the RFs were screened based on Lasso-CV algorithm, and the Rad_score was calculated. The Clinic factors were screened by univariate and multivariate logistic regression analysis of the clinical and pathological factors and CT characteristics. The Rad_score, Clinic、Rad_score + Clinic nomograms were constructed and verified using the test dataset. The performance, discrimination power and calibration of the nomograms were compared, and their clinical value was evaluated using decision curve analysis.

RESULTS

Six RFs were retained to calculate the Rad_score. The Clinic factors included Rad_score, KPS score, platelet, calcification and TNM clinical stage. In terms of discrimination, the Rad_score + Clinic nomogram showed better performance (AUC=0.84 for training set; AUC=0.85 for test set) than the Rad_score nomogram (AUC=0.78 for training set, P=0.029; AUC=0.77 for Test set, P=0.025) and Clinic nomogram (AUC=0.77 for training set, P=0.014; AUC=0.77 for test set, P=0.011). In terms of calibration, the P value for goodness of fit test of the Rad_score+Clinic nomogram was 0.065 for the training set and 0.628 for the test set. Decision curve analysis showed a greater clinical value of the Rad_score+Clinic nomogram with Rad_score than the Clinic nomogram without Rad_score.

CONCLUSION

The nomogram based on preoperative CT RFs has a high value for predicting postoperative recurrence of stage Ⅰ-Ⅲ ccRCC to facilitate individualized treatment of RCC.

[Risk factors of occurrence and treatment failure of peritoneal dialysis-associated polymicrobial peritonitis: a multicenter retrospective study]

OBJECTIVE

To determine the risk factors of occurrence and treatment failure of peritoneal dialysis associatedperitonitis (PDAP) due to polymicrobial infections.

METHODS

We retrospectively collected the clinical data of patients with PDAP from the peritoneal dialysis (PD) centers in 4 general hospitals in Jilin Province from 2013 to 2019. The patients were divided, according to the results of peritoneal dialysate culture, into polymicrobial PDAP group and control group for comparison of the clinical data, treatment outcomes, and long-term prognosis. The independent risk factors of the occurrence and treatment failure of polymicrobial PDAP were explored using multivariate regression analysis.

RESULTS

We recruited a total of 625 patients from the 4 PD centers, among whom 1085 episodes of PDAP were recorded. Polymicrobial PDAP accounted for 7.6% of the total PDAP episodes, and this proportion increased from 5.3% in 2013-2016 to 9.4% in 2017-2019 (P= 0.012). Compared with the control group, polymicrobial PDAP group had higher proportions of elderly patients and patients with refractory PDAP, with greater white blood cell counts in the first-day dialysate and longer course of antibiotic treatment (P < 0.05). The risk of catheter removal and treatment failure (catheter removal or PDAP-related death) in polymicrobial PDAP group was 2.972 times (OR=2.972, 95% CI: 1.634-5.407, P < 0.001) and 2.692 times (OR=2.692, 95% CI: 1.578-4.591, P < 0.001) that in the control group, respectively. The risk of withdrawal from PD (technical failure + all-cause death) was 1.5- fold higher in polymicrobial PDAP group than that in the control group (OR=1.500, 95% CI: 1.085-2.074, P=0.014). Elderly patients (>65 years) had a 1.937-fold higher risk of experiencing polymicrobial PDAP than younger patients (OR=1.937, 95% CI: 1.207-3.109, P= 0.006). Diabetes mellitus (OR=5.554, 95% CI: 1.021-30.201, P=0.047), mixed fungal infeciton (OR=343.687, 95% CI: 21.554- 5480.144, P < 0.001), and Pseudomonas aeruginosa infection (OR=11.518, 95% CI: 1.632 to 81.310, P=0.014) were associated with increased risks of treatment failure by 4.554, 342.687 and 10.518 times, respectively.

CONCLUSION

The proportion of polymicrobial PDAP in the total PDAP cases tends to increase in recent years. Polymicrobial infection is an independent risk factor of both treatment failure and poor prognosis in patients with PDAP. An old age is an independent risk factor for polymicrobial PDAP, while diabetes mellitus and infections with mixed fungi or Pseudomonas aeruginosa are independent risk factors for treatment failure.

Impact of COVID-19 Pandemic on Use of Telemedicine Services in an Academic Medical Center

The COVID-19 pandemic changed the landscape of telehealth services. The goal of this paper was to identify demographic groups of patients who have used telemedicine services before and after the start of the pandemic, and to analyze how different demographic groups' telehealth usage patterns change throughout the course of the pandemic. A de-identified study dataset was generated by querying electronic health records at the Mount Sinai Health System to identify all patients. 129,625 patients were analyzed. Demographic shifts in patients seeking telemedicine service were identified. There was significant increase in the middle age and older population using telehealth services. During the pandemic use of telemedicine services was increased among male patients and racial minority patients. Furthermore, telehealth services had expanded to a broader spectrum of medical specialties.

[Treatment of lumbar degenerative diseases with recapping laminoplasty and nerve root canal's decompression preserving the continuity of supraspinous ligament]

Objective: To explore the clinical effect of lumbar discectomy and nerve root canal's enlargement preserving the continuity of supraspinous ligament in the treatment of lumbar degenerative disease. Methods: The data of patients with lumbar degenerative disease who underwent operation from 2016 to 2018 were analyzed retrospectively, and the patients were divided into two groups according to the different operation. The treatment group (17 cases) was treated with recapping laminoplasty, lumbar discectomy and nerve root canal's enlargement, and the control group (28 cases) was treated with total laminectomy, nerve root canal's enlargement, lumbar discectomy, interbody fusion and internal fixation (PLIF). All patients were followed up for 12 to 27 months (mean 17.8 months). Japanese Orthopaedic Association Scores(JOA) and visual analogue scale(VAS) of pain were used to evaluate the clinical effect before and after the operation, lumbar dynamical X-ray and Cobb angle were collecting for imaging evaluation, and the adjacent segment degeneration at the last follow-up was recorded. Results: There was no significant difference in preoperative JOA score, VAS score and Lumbar Cobb angle between the two groups (all P>0.05). The operation time in the treatment group was shorter than that in the control group, and the blood loss during operation in the treatment group was lower than that in the control group, the bed rest time of the treatment group after operation was shorter than that in the control group ((79±14) vs (118±17) min, (151±38) vs (324±70) ml and (3.4±0.7) vs (4.3±1.0) d,respectively; t=-8.508, -10.724, -3.244, all P<0.01). In addition, compared with the control group, the volume of postoperative drainage in the treatment group also decreased significantly (t=-5.637, P<0.01). There was no significant difference in JOA score between the two groups 1 year after the operation (P>0.05), but there was significant difference in VAS score between the two groups, the treatment group was better than the control group (P<0.05). Compared with the control group, the lumbar Cobb angle in the treatment group increased significantly one year after the operation (55.3°±3.2° vs 38.4°±6.2°, t=10.391, P<0.05). During the follow-up, no loosening or fracture of the implants was found in all patients. Conclusion: Treatment of lumbar degenerative diseases with recapping laminoplasty and nerve root canal's decompression preserving the continuity of supraspinous ligament by ultrasound osteotome has the same clinical effect as PLIF. It has the advantages of shortening operation time, less bleeding, better maintenance of lumbar lordosis after operation and reduction of adjacent segment degeneration.

Latent COVID-19 Clusters in Patients with Chronic Respiratory Conditions

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

[A novel trans-muscular approach for microsurgical bilateral decompression via unilateral approach for degenerative lumbar stenosis: a series study]

Objective: To compare clinical outcomes of microsurgical bilateral decompression via unilateral approach (MBDU) between trans longissimus and iliocostalis approach, a novel lateral paraspinal approach(LPA), and classic Wiltseapproach for single level degenerative lumbar stenosis. Methods: LPA approach was researched by imaging measurement and cadaver anatomy. Retrospective analysis of a total 124 cases(male/female 75/49, aged(55±14) years) received single-level MBDU due to degenerative lumbar stenosis from 2016 to 2018 in Beijing Tiantan Hospital. Patients were categorized into two groups according to spinal canal morphology. Group A: trefoil shape (n=48), received MBDU via LPA; Group B: round & oval shape (n=76), received MBDU via Wiltse approach. Clinical parameters, including visual analogue scale (VAS) of pain, OswestryDability Index (ODI), satisfaction rate were assessed before the operation and at the latest follow-up. The internal fixation and intervertebral fusion were evaluated by X-ray and CT.The data were compared with single sample t test between the two groups. Results: The feasibility of trans longissimus and iliocostalis approach was proved by imaging measurement and anatomic study. The tube's maximum tilt angle in LPA group was 65°, and it was 40°in Wiltse group. There was no significantly difference in the operating time, bleeding and drainage volume between the two groups((120±27) min vs (115±32) min, (104±31) ml vs (110±41) ml, (50±15) ml vs (47±18) ml, respectively, t=1.246,-1.917,1.730,all P>0.05). In both groups, VAS and ODI scores significantly improved at the latest follow-up when compared with those before the operation (all P<0.05), but those were all comparable between the two groups(all P>0.05). The excellent rate of subjective satisfaction for the clinical efficacy in group A and B was 89.6%(43/48) and 86.8%(66/76) respectively (χ(2)=0.208,P>0.05). Conclusions: LPA approachfor microsurgical bilateral decompression via unilateral approach (MBDU) in degenerative lumbar stenosis is safe and effective, it has advantages in direct vison of contralateral nerve root canal decompression for trefoil shaped spinal canal.

Genotype-phenotype correlation in 75 patients with small supernumerary marker chromosomes

Background

Small supernumerary marker chromosomes (sSMCs) are rare structural abnormalities in the population; however, they are frequently found in children or fetuses with hypoevolutism and infertile adults. sSMCs are usually observed first by karyotyping, and further analysis of their molecular origin is important in clinical practice. Next-generation sequencing (NGS) combined with Sanger sequencing helps to identify the chromosomal origins of sSMCs and correlate certain sSMCs with a specific clinical picture.

Results

Karyotyping identified 75 sSMCs in 74,266 samples (0.1% incidence). The chromosomal origins of 27 of these sSMCs were detected by sequencing-related techniques (NGS, MLPA and STR). Eight of these sSMCs are being reported for the first time. sSMCs mainly derived from chromosomal X, Y, 15, and 18, and some sSMC chromosomal origins could be correlated with clinical phenotypes. However, the chromosomal origins of the remaining 48 sSMC cases are unknown. Thus, we will develop a set of economical and efficient methods for clinical sSMC diagnosis.

Conclusions

This study details the comprehensive characterization of 27 sSMCs. Eight of these sSMCs are being reported here for the first time, providing additional information to sSMC research. Identifying sSMCs may reveal genotype-phenotype correlations and integrate genomic data into clinical care.