The C-terminus of Bienertia sinuspersici Toc159 contains essential elements for its targeting and anchorage to the chloroplast outer membrane

Most nucleus-encoded chloroplast proteins rely on an N-terminal transit peptide (TP) as a post-translational sorting signal for directing them to the organelle. Although Toc159 is known to be a receptor for specific preprotein TPs at the chloroplast surface, the mechanism for its own targeting and integration into the chloroplast outer membrane is not completely understood. In a previous study, we identified a novel TP-like sorting signal at the C-terminus (CT) of a Toc159 homolog from the single-cell C4 species, Bienertia sinuspersici. In the current study, we have extended our understanding of the sorting signal using transient expression of fluorescently-tagged fusion proteins of variable-length, and with truncated and swapped versions of the CT. As was shown in the earlier study, the 56 residues of the CT contain crucial sorting information for reversible interaction of the receptor with the chloroplast envelope. Extension of this region to 100 residues in the current study stabilized the interaction via membrane integration, as demonstrated by more prominent plastid-associated signals and resistance of the fusion protein to alkaline extraction. Despite a high degree of sequence similarity, the plastid localization signals of the equivalent CT regions of Arabidopsis thaliana Toc159 homologs were not as strong as that of the B. sinuspersici counterparts. Together with computational and circular dichroism analyses of the CT domain structures, our data provide insights into the critical elements of the CT for the efficient targeting and anchorage of Toc159 receptors to the dimorphic chloroplasts in the single-cell C4 species.published_or_final_versio

Dengue outbreak 2019: clinical and laboratory profiles of dengue virus infection in Dhaka city.

BACKGROUND: Dengue fever has been one of the most common mosquito-transmitted diseases in the world, affecting more than 128 countries in both tropical and subtropical regions. Bangladesh has been sufferring from dengue outbreaks almost annually since 2000, and in 2019, Bangladesh faced the worst outbreak of dengue to date. This study aimed to provide clinical and biochemical profiles of Bangladesh's dengue-infected patients. METHODS: This cross-sectional study was conducted from August through December 2019 in three tertiary private hospitals in Dhaka, Bangladesh. We collected information on demographic data, clinical characteristics, and laboratory profiles for 542 confirmed hospitalized acute dengue cases using a structured questionnaire. RESULTS: The average age of the enrolled patients was 26.15 years, and about 50% of patients belonged to the age group of 20-40 years. The most frequent among the prevalent clinical symptoms were fever (93.1%), abdominal pain (29.5%), skin rash (25.3%), and diarrhea (19.7%). 316 patients had some complications, such as breathing problems (41.4%), pleural effusion (38.9%), gum bleeding (11.1%), etc. More than 90% of the patients showed seropositivity for the DENV-NS1 antigen. CONCLUSIONS: Over the last couple of years, dengue fever has become a major health issue for Bangladesh. To reduce the burden of this disease, timely diagnosis and prompt treatment are necessary. This analysis thus yields the clinical features, laboratory profiles, and seropositivity test results of dengue patients from Bangladesh. The research results may help clinicians understand the circumstantial diagnosis of dengue patients and facilitate early intervention

Walking Activity Recognition with sEMG Sensor Array on Thigh Circumference using Convolutional Neural Network

In recognition of walking gait modes using surface electromyography (sEMG), the use of sEMG sensor array can provide sensor redundancy and less rigorous identification of sEMG electrode placements as compared to the conventional sEMG electrode placements right in the middle of muscle bellies. However, the potentially lesser discriminative and noisier sEMG signals from the sEMG sensor array pose the challenge in developing accurate and robust machine learning classifier for walking activity recognition. In this paper, we explore the use of convolution neural network (CNN) classifier with frequency gradient feature derived from EMG signal spectrogram for detecting different walking activities using an sEMG sensor array on thigh circumference. EMG dataset from five healthy subjects and an amputee for five walking activities namely walking at slow, normal and fast speed, ramp ascending and ramp descending are used to train and test the CNN-based classifier. Our preliminary findings suggest that frequency gradient feature can improve the CNN-based classifier performance for walking activity recognition using EMG sensor array on thigh circumference

Prevalence of Cardiovascular Disease in Patients With Potentially Curable Malignancies: A National Registry Dataset Analysis.

BACKGROUND: Although a common challenge for patients and clinicians, there is little population-level evidence on the prevalence of cardiovascular disease (CVD) in individuals diagnosed with potentially curable cancer. OBJECTIVES: We investigated CVD rates in patients with common potentially curable malignancies and evaluated the associations between patient and disease characteristics and CVD prevalence. METHODS: The study included cancer registry patients diagnosed in England with stage I to III breast cancer, stage I to III colon or rectal cancer, stage I to III prostate cancer, stage I to IIIA non-small-cell lung cancer, stage I to IV diffuse large B-cell lymphoma, and stage I to IV Hodgkin lymphoma from 2013 to 2018. Linked hospital records and national CVD databases were used to identify CVD. The rates of CVD were investigated according to tumor type, and associations between patient and disease characteristics and CVD prevalence were determined. RESULTS: Among the 634,240 patients included, 102,834 (16.2%) had prior CVD. Men, older patients, and those living in deprived areas had higher CVD rates. Prevalence was highest for non-small-cell lung cancer (36.1%) and lowest for breast cancer (7.7%). After adjustment for age, sex, the income domain of the Index of Multiple Deprivation, and Charlson comorbidity index, CVD remained higher in other tumor types compared to breast cancer patients. CONCLUSIONS: There is a significant overlap between cancer and CVD burden. It is essential to consider CVD when evaluating national and international treatment patterns and cancer outcomes

Prevalence of Cardiovascular Disease in Patients With Potentially Curable Malignancies: A National Registry Dataset Analysis

Background: Although a common challenge for patients and clinicians, there is little population-level evidence on the prevalence of cardiovascular disease (CVD) in individuals diagnosed with potentially curable cancer. Objectives: We investigated CVD rates in patients with common potentially curable malignancies and evaluated the associations between patient and disease characteristics and CVD prevalence. Methods: The study included cancer registry patients diagnosed in England with stage I to III breast cancer, stage I to III colon or rectal cancer, stage I to III prostate cancer, stage I to IIIA non-small-cell lung cancer, stage I to IV diffuse large B-cell lymphoma, and stage I to IV Hodgkin lymphoma from 2013 to 2018. Linked hospital records and national CVD databases were used to identify CVD. The rates of CVD were investigated according to tumor type, and associations between patient and disease characteristics and CVD prevalence were determined. Results: Among the 634,240 patients included, 102,834 (16.2%) had prior CVD. Men, older patients, and those living in deprived areas had higher CVD rates. Prevalence was highest for non-small-cell lung cancer (36.1%) and lowest for breast cancer (7.7%). After adjustment for age, sex, the income domain of the Index of Multiple Deprivation, and Charlson comorbidity index, CVD remained higher in other tumor types compared to breast cancer patients. Conclusions: There is a significant overlap between cancer and CVD burden. It is essential to consider CVD when evaluating national and international treatment patterns and cancer outcomes

Do cerebrospinal fluid transfer methods affect measured amyloid β42, total tau, and phosphorylated tau in clinical practice?

Introduction Cerebrospinal fluid (CSF) neurodegenerative markers are measured clinically to support a diagnosis of Alzheimer's disease. Several preanalytical factors may alter the CSF concentrations of amyloid β 1–42 (Aβ1–42) in particular with the potential to influence diagnosis. We aimed to determine whether routine handling of samples alters measured biomarker concentration compared with that of prompt delivery to the laboratory. Methods Forty individuals with suspected neurodegenerative diseases underwent diagnostic lumbar punctures using a standardized technique. A sample of each patient's CSF was sent to the laboratory by four different delivery methods: (1) by courier at room temperature; (2) by courier, on ice; (3) using standard hospital portering; and (4) after quarantining for >24 hours. Aβ1–42, total tau (t‐tau), and phosphorylated tau (p‐tau) levels measured using standard enzyme‐linked immunosorbent assay techniques were compared between transfer methods. Results There were no significant differences in Aβ1–42, t‐tau, or p‐tau concentrations measured in samples transported via the different delivery methods despite significant differences in time taken to deliver samples. Discussion When CSF is collected in appropriate tubes, transferred at room temperature, and processed within 24 hours, neurodegenerative markers can be reliably determined

Case-ascertainment of acute myocardial infarction hospitalizations in cancer patients: A cohort study using English linked electronic health data

Aims: To assess the recording and accuracy of acute myocardial infarction (AMI) hospital admissions between two electronic health record databases within an English cancer population over time and understand the factors that affect case-ascertainment. Methods and results: We identified 112 502 hospital admissions for AMI in England 2010-2017 from the Myocardial Ischaemia National Audit Project (MINAP) disease registry and hospital episode statistics (HES) for 95 509 patients with a previous cancer diagnosis up to 15 years prior to admission. Cancer diagnoses were identified from the National Cancer Registration Dataset (NCRD). We calculated the percentage of AMI admissions captured by each source and examined patient characteristics associated with source of ascertainment. Survival analysis assessed whether differences in survival between case-ascertainment sources could be explained by patient characteristics. A total of 57 265 (50.9%) AMI admissions in patients with a prior diagnosis of cancer were captured in both MINAP and HES. Patients captured in both sources were younger, more likely to have ST-segment elevation myocardial infarction and had better prognosis, with lower mortality rates up to 9 years after AMI admission compared with patients captured in only one source. The percentage of admissions captured in both data sources improved over time. Cancer characteristics (site, stage, and grade) had little effect on how AMI was captured. Conclusion: MINAP and HES define different populations of patients with AMI. However, cancer characteristics do not substantially impact on case-ascertainment. These findings support a strategy of using multiple linked data sources for observational cardio-oncological research into AMI

Impact of a Prior Cancer Diagnosis on Quality of Care and Survival Following Acute Myocardial Infarction: Retrospective Population-Based Cohort Study in England

BACKGROUND: An increasing proportion of patients with cancer experience acute myocardial infarction (AMI). We investigated differences in quality of AMI care and survival between patients with and without previous cancer diagnoses. METHODS: A retrospective cohort study using Virtual Cardio-Oncology Research Initiative data. Patients aged 40+ years hospitalized in England with AMI between January 2010 and March 2018 were assessed, ascertaining previous cancers diagnosed within 15 years. Multivariable regression was used to assess effects of cancer diagnosis, time, stage, and site on international quality indicators and mortality. RESULTS: Of 512 388 patients with AMI (mean age, 69.3 years; 33.5% women), 42 187 (8.2%) had previous cancers. Patients with cancer had significantly lower use of ACE (angiotensin-converting enzyme) inhibitors/angiotensin receptor blockers (mean percentage point decrease [mppd], 2.6% [95% CI, 1.8–3.4]) and lower overall composite care (mppd, 1.2% [95% CI, 0.9–1.6]). Poorer quality indicator attainment was observed in patients with cancer diagnosed in the last year (mppd, 1.4% [95% CI, 1.8–1.0]), with later stage disease (mppd, 2.5% [95% CI, 3.3–1.4]), and with lung cancer (mppd, 2.2% [95% CI, 3.0–1.3]). Twelve-month all-cause survival was 90.5% in noncancer controls and 86.3% in adjusted counterfactual controls. Differences in post-AMI survival were driven by cancer-related deaths. Modeling improving quality indicator attainment to noncancer patient levels showed modest 12-month survival benefits (lung cancer, 0.6%; other cancers, 0.3%). CONCLUSIONS: Measures of quality of AMI care are poorer in patients with cancer, with lower use of secondary prevention medications. Findings are primarily driven by differences in age and comorbidities between cancer and noncancer populations and attenuated after adjustment. The largest impact was observed in recent cancer diagnoses (<1 year) and lung cancer. Further investigation will determine whether differences reflect appropriate management according to cancer prognosis or whether opportunities to improve AMI outcomes in patients with cancer exist

Walking Activity Recognition with sEMG Sensor Array on Thigh Circumference using Convolutional Neural Network

In recognition of walking gait modes using surface electromyography (sEMG), the use of sEMG sensor array can provide sensor redundancy and less rigorous identification of sEMG electrode placements as compared to the conventional sEMG electrode placements right in the middle of muscle bellies. However, the potentially lesser discriminative and noisier sEMG signals from the sEMG sensor array pose the challenge in developing accurate and robust machine learning classifier for walking activity recognition. In this paper, we explore the use of convolution neural network (CNN) classifier with frequency gradient feature derived from EMG signal spectrogram for detecting different walking activities using an sEMG sensor array on thigh circumference. EMG dataset from five healthy subjects and an amputee for five walking activities namely walking at slow, normal and fast speed, ramp ascending and ramp descending are used to train and test the CNN-based classifier. Our preliminary findings suggest that frequency gradient feature can improve the CNN-based classifier performance for walking activity recognition using EMG sensor array on thigh circumference.<br/