Mobilization of innate and adaptive antitumor immune responses by the RNP-targeting antibody ATRC-101.

Immunotherapy approaches focusing on T cells have provided breakthroughs in treating solid tumors. However, there remains an opportunity to drive anticancer immune responses via other cell types, particularly myeloid cells. ATRC-101 was identified via a target-agnostic process evaluating antibodies produced by the plasmablast population of B cells in a patient with non-small cell lung cancer experiencing an antitumor immune response during treatment with checkpoint inhibitor therapy. Here, we describe the target, antitumor activity in preclinical models, and data supporting a mechanism of action of ATRC-101. Immunohistochemistry studies demonstrated tumor-selective binding of ATRC-101 to multiple nonautologous tumor tissues. In biochemical analyses, ATRC-101 appears to target an extracellular, tumor-specific ribonucleoprotein (RNP) complex. In syngeneic murine models, ATRC-101 demonstrated robust antitumor activity and evidence of immune memory following rechallenge of cured mice with fresh tumor cells. ATRC-101 increased the relative abundance of conventional dendritic cell (cDC) type 1 cells in the blood within 24 h of dosing, increased CD8+ T cells and natural killer cells in blood and tumor over time, decreased cDC type 2 cells in the blood, and decreased monocytic myeloid-derived suppressor cells in the tumor. Cellular stress, including that induced by chemotherapy, increased the amount of ATRC-101 target in tumor cells, and ATRC-101 combined with doxorubicin enhanced efficacy compared with either agent alone. Taken together, these data demonstrate that ATRC-101 drives tumor destruction in preclinical models by targeting a tumor-specific RNP complex leading to activation of innate and adaptive immune responses.

Enduring undetectable MRD and updated outcomes in relapsed/refractory CLL after fixed-duration venetoclax-rituximab.

The MURANO trial (A Study to Evaluate the Benefit of Venetoclax Plus Rituximab Compared With Bendamustine Plus Rituximab in Participants With Relapsed or Refractory Chronic Lymphocytic Leukemia [CLL]; ClinicalTrials.gov identifier #NCT02005471) reported superior progression-free survival (PFS) and overall survival (OS) with venetoclax-rituximab (VenR) vs bendamustine-rituximab (BR) in relapsed/refractory (R/R) CLL. Patients were randomized to 2 years of VenR (n = 194; rituximab for the first 6 months) or 6 months of BR (n = 195). Although undetectable minimal residual disease (uMRD) was achieved more often with VenR, the long-term implications of uMRD with this fixed-duration, chemotherapy-free regimen have not been explored. We report MRD kinetics and updated outcomes with 5 years' follow-up. Survival benefits with VenR vs BR were sustained (median PFS [95% confidence interval]: 53.6 [48.4, 57.0] vs 17.0 [15.5, 21.7] months, respectively, P < .0001; 5-year OS [95% confidence interval]: 82.1% [76.4, 87.8] vs 62.2% [54.8, 69.6], P < .0001). VenR was superior to BR, regardless of cytogenetic category. VenR-treated patients with uMRD at end of treatment (EOT; n = 83) had superior OS vs those with high-MRD+ (n = 12): 3-year post-EOT survival rates were 95.3% vs 72.9% (P = .039). In those with uMRD at EOT, median time to MRD conversion was 19.4 months. Of 47 patients with documented MRD conversion, 19 developed progressive disease (PD); median time from conversion to PD was 25.2 months. A population-based logistic growth model indicated slower MRD median doubling time post-EOT with VenR (93 days) vs BR (53 days; P = 1.2 × 10-7). No new safety signals were identified. Sustained survival, uMRD benefits, and durable responses support 2-year fixed-duration VenR treatment in R/R CLL.

Cerebral Amyloid Angiopathy in Patients with Cognitive Impairment: Cerebrospinal Fluid Biomarkers.

INTRODUCTION: Cerebral amyloid angiopathy (CAA) is characterized by amyloid ß (Aß) deposition in brain vessels, leading to hemorrhagic phenomena and cognitive impairment. Magnetic resonance imaging (MRI)-based criteria allow a diagnosis of probable CAA in vivo, but such a diagnosis cannot predict the eventual development of CAA. METHODS: We conducted a retrospective cohort study of 464 patients with cognitive disorders whose data were included in a brain health biobank. De-identified parameters including sex, age, cognitive score, APOE status, and cerebrospinal fluid (CSF) levels of Aß 1-40, Aß 1-42, phosphorylated tau, and total tau were assessed in those with and without CAA. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined. RESULTS: CAA was present in 53 of 464 (11.5%) patients. P-tau level was significantly higher in those with CAA (115 vs. 84.3 pg/mL p = 0.038). In univariate analyses, the risk of developing CAA was higher with increased age (OR, 1.036; 95% CI: 1.008, 1.064; p = 0.011) and decreased CSF level of Aß 1-40 (OR, 0.685; 95% CI: 0.534, 0.878; p = 0.003). In multivariate analyses, the risk of CAA remained higher with a decreased CSF level of Aß 1-40 (OR, 0.681; 95% CI: 0.531, 0.874; p = 0.003). CONCLUSION: These findings suggest that Aß 1-40 levels in the CSF might be a useful molecular biomarker of CAA in patients with dementia.

The #IVF journey: content analysis of IVF videos on TikTok.

RESEARCH QUESTION: What patient populations and outcomes are depicted on IVF videos on the short-form video-sharing application 'TikTok'? DESIGN: This cross-sectional study investigated the 100 most-viewed videos utilizing the #IVF hashtag on TikTok. Only videos in English or Spanish were included in the analysis. An automated web-scraping platform was used to extract information from these videos. Standardized video coding was used to evaluate the featured subject(s), phase of care and IVF outcomes from the videos. RESULTS: Videos meeting the inclusion criteria (n=93) had 731 million views, 91 million likes and 893,000 shares. Patients' personal stories about IVF are over-represented relative to videos created by healthcare professionals. #IVF videos popular on TikTok disproportionately depict same-sex couples (38.1%), gestational carriers (14.0%), multiple gestations (60.0%) and live births (89.3%) relative to real-world data. Among the videos making scientific claims (n=16), nearly all were moderately to highly accurate (93.8%). Most videos had a positive (55.9%) or neutral (5.5%) tone towards IVF. CONCLUSIONS: Popular #IVF videos on TikTok contribute to public discourse about infertility, and highlight demographics that have traditionally been under-represented in health care. Videos rarely focused on health education or made informational claims, with low scores on measures of quality of information and actionability. This gap represents an opportunity for health providers and educators to create more engaging and educational content to reach patients considering IVF.

High-Fidelity Indirect Readout of Trapped-Ion Hyperfine Qubits.

We propose and demonstrate a protocol for high-fidelity indirect readout of trapped ion hyperfine qubits, where the state of a ^{9}Be^{+} qubit ion is mapped to a ^{25}Mg^{+} readout ion using laser-driven Raman transitions. By partitioning the ^{9}Be^{+} ground-state hyperfine manifold into two subspaces representing the two qubit states and choosing appropriate laser parameters, the protocol can be made robust to spontaneous photon scattering errors on the Raman transitions, enabling repetition for increased readout fidelity. We demonstrate combined readout and back-action errors for the two subspaces of 1.2_{-0.6}^{+1.1}×10^{-4} and 0_{-0}^{+1.9}×10^{-5} with 68% confidence while avoiding decoherence of spectator qubits due to stray resonant light that is inherent to direct fluorescence detection.

Resource-Efficient Dissipative Entanglement of Two Trapped-Ion Qubits.

We demonstrate a simplified method for dissipative generation of an entangled state of two trapped-ion qubits. Our implementation produces its target state faster and with higher fidelity than previous demonstrations of dissipative entanglement generation and eliminates the need for auxiliary ions. The entangled singlet state is generated in ∼7 ms with a fidelity of 0.949(4). The dominant source of infidelity is photon scattering. We discuss this error source and strategies for its mitigation.

Perioperative Morbidity and Mortality of Patients With COVID-19 Who Undergo Urgent and Emergent Surgical Procedures.

OBJECTIVE: To evaluate the perioperative morbidity and mortality of patients with COVID-19 who undergo urgent and emergent surgery. SUMMARY BACKGROUND DATA: Although COVID-19 infection is usually associated with mild disease, it can lead to severe respiratory complications. Little is known about the perioperative outcomes of patients with COVID-19. METHODS: We examined patients who underwent urgent and emergent surgery at 2 hospitals in New York City from March 17 to April 15, 2020. Elective surgical procedures were cancelled throughout and routine, laboratory based COVID-19 screening was instituted on April 1. Mortality, complications, and admission to the intensive care unit were compared between patients with COVID-19 detected perioperatively and controls. RESULTS: Among 468 subjects, 36 (7.7%) had confirmed COVID-19. Among those with COVID-19, 55.6% were detected preoperatively and 44.4% postoperatively. Before the routine preoperative COVID-19 laboratory screening, 7.7% of cases were diagnosed preoperatively compared to 65.2% after institution of screening (P = 0.0008). The perioperative mortality rate was 16.7% in those with COVID-19 compared to 1.4% in COVID-19 negative subjects [aRR = 9.29; 95% confidence interval (CI), 5.68-15.21]. Serious complications were identified in 58.3% of COVID-19 subjects versus 6.0% of controls (aRR = 7.02; 95%CI, 4.96-9.92). Cardiac arrest, sepsis/shock, respiratory failure, pneumonia, acute respiratory distress syndrome, and acute kidney injury were more common in those with COVID-19. The intensive care unit admission rate was 36.1% in those with COVID-19 compared to 16.4% of controls (aRR = 1.34; 95%CI, 0.86-2.09). CONCLUSIONS: COVID-19 is associated with an increased risk for serious perioperative morbidity and mortality. A substantial number of patients with COVID-19 are not identified until after surgery.

Interactions between PD-1 and PD-L1 promote tolerance by blocking the TCR-induced stop signal.

Programmed death 1 (PD-1) is an inhibitory molecule expressed on activated T cells; however, the biological context in which PD-1 controls T cell tolerance remains unclear. Using two-photon laser-scanning microscopy, we show here that unlike naive or activated islet antigen-specific T cells, tolerized islet antigen-specific T cells moved freely and did not swarm around antigen-bearing dendritic cells (DCs) in pancreatic lymph nodes. Inhibition of T cell antigen receptor (TCR)-driven stop signals depended on continued interactions between PD-1 and its ligand, PD-L1, as antibody blockade of PD-1 or PD-L1 resulted in lower T cell motility, enhanced T cell-DC contacts and caused autoimmune diabetes. Blockade of the immunomodulatory receptor CTLA-4 did not alter T cell motility or abrogate tolerance. Thus, PD-1-PD-L1 interactions maintain peripheral tolerance by mechanisms fundamentally distinct from those of CTLA-4.

Quantum Harmonic Oscillator Spectrum Analyzers.

Characterization and suppression of noise are essential for the control of harmonic oscillators in the quantum regime. We measure the noise spectrum of a quantum harmonic oscillator from low frequency to near the oscillator resonance by sensing its response to amplitude modulated periodic drives with a qubit. Using the motion of a trapped ion, we experimentally demonstrate two different implementations with combined sensitivity to noise from 500 Hz to 600 kHz. We apply our method to measure the intrinsic noise spectrum of an ion trap potential in a previously unaccessed frequency range.

Less radical surgery for early-stage cervical cancer: a systematic review.

OBJECTIVE: A systematic review was performed to examine the outcomes of simple hysterectomy for women with low-risk, early-stage cervical cancer. DATA SOURCES: MEDLINE, Embase, Web of Science, and ClinicalTrials.gov were searched from inception until November 4, 2020. STUDY ELIGIBILITY CRITERIA: Original research reporting recurrence or survival outcomes among women with early-stage cervical cancer (defined as stage IA2 to IB1 disease) who were treated with simple hysterectomy. METHODS: Data regarding study characteristics, tumor characteristics, other treatment modalities, adjuvant therapy, recurrence, and survival outcomes were analyzed. Studies that reported both simple hysterectomy and radical hysterectomy outcomes were compared in a subgroup analysis. Summary statistics were reported and eligible studies were further analyzed to determine an estimated hazard ratio comparing simple hysterectomy with radical hysterectomy. RESULTS: A total of 21 studies were included, of which 3 were randomized control trials, 14 retrospective studies, 2 prospective studies, and 2 population-level data sets. The cohort included 2662 women who underwent simple hysterectomy, of which 36.1% had stage IA2 disease and 61.0% stage IB1 disease. Most cases (96.8%) involved tumors of ≤2 cm in size, and 15.4% of cases were lymphovascular space invasion positive. Approximately 71.8% of women who underwent simple hysterectomy had a lymph node assessment, and 30.7% of women underwent adjuvant chemotherapy or radiation. The most common complications described were lymphedema (24%), lymphocysts (22%), and urinary incontinence (18.5%). The total death rate for studies that reported deaths was 5.5%. By stage, there was a 2.7% mortality rate among IA2 disease and a 7.3% mortality rate among IB1 disease. Of note, 18 studies reported outcomes for both simple and radical hysterectomy, with a 4.5% death rate in the radical hysterectomy group and a 5.8% death rate in the simple hysterectomy group. Estimated and reported hazard ratio demonstrated no significant association for mortality between radical and nonradical surgeries for IA2 disease but potentially increased risk of mortality among IB1 disease. All studies had a moderate to high risk of bias, including the 3 randomized control trials. Level of evidence was limited to III to IV. CONCLUSION: The use of less radical surgery for women with stage IA2 and small volume IB1 cervical cancers appears favorable. However, there is concern that simple hysterectomy in women with stage IB1 tumors may adversely impact survival. Overall, the quality of studies available is modest, limiting the conclusions that can be drawn from the available literature.

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

BACKGROUND: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country's policy measures. OBJECTIVE: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

[This corrects the article DOI: 10.2196/27806.].

Coronavirus Spectrum Infections (COVID-19, MERS, SARS) in Cancer Patients: A Systematic Review of the Literature.

BACKGROUND: Coronavirus 2019 (COVID-19) has spread rapidly around the world to become a global pandemic. There is limited data on the impact of COVID-19 among patients with cancer. METHODS: A systematic review was performed to determine outcomes of adult patients with cancer affected by coronavirus infections, specifically SARS, MERS, and COVID-19. Studies were independently screened by two reviewers and assessed for quality and bias. Outcomes measured included study characteristics, cancer type, phase of care at the time of diagnosis, and clinical presentation. Morbidity and mortality outcomes were analyzed to assess the severity of infection as compared to the general population. RESULTS: A total of 19 studies with 110 patients were included. Of these, 66.4% had COVID-19 infections, 32.7% MERS and only one patient with SARS. The majority of COVID-19 studies were based on studies in China. There was a 56.6% rate of a severe event, including ICU admission or requiring mechanical ventilation, with an overall 44.5% fatality rate. CONCLUSIONS: Patients with cancer with coronavirus infections may be more susceptible to higher morbidity and mortality.

The effect of guideline-concordant care in mitigating insurance status disparities in cervical cancer.

OBJECTIVE: To evaluate whether the receipt of evidence-based care could mitigate survival disparities among Medicaid recipients and uninsured women with cervical cancer. METHODS: The National Cancer Database was utilized to identify women with cervical cancer treated from 2004 to 2016. Eight quality metrics were determined. Survival outcomes were examined stratified by insurance status and stage. To measure the impact of guideline-concordant care on the mitigation of disparities, we compared survival outcomes of the overall cohort to one that was perfectly adherent to all quality metrics. RESULTS: A total of 103,400 patients were identified; 47.0% of patients had private insurance, 21.5% Medicaid and 9.2% uninsured. Medicaid and uninsured patients were significantly less likely than privately insured patients to receive timely completion of radiation and timely initiation of treatment; uninsured patients were also significantly less likely to receive treatment for locally advanced disease. Medicaid and uninsured patients were also less likely to receive lymph node assessment and primary chemoradiation. Medicaid and uninsured patients had an increased risk of mortality compared to privately insured patients (aHR = 1.36, 95% CI 1.31-1.41 and aHR 1.29, 95% CI 1.23-1.36 respectively). While the receipt of these quality metrics was associated with improved survival, Medicaid and uninsured women who received guideline-concordant care were still at an increased risk of death compared to women with private insurance (aHR = 1.38, 95% CI 1.35-1.49 and aHR = 1.24; 95% CI, 1.16-1.32 respectively). CONCLUSION: Medicaid and uninsured patients were less likely to receive evidence-based care and were at increased risk of mortality at all stages compared to privately insured patients. The receipt of quality care does not eliminate insurance status-based disparities among women with cervical cancer.

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

BACKGROUND: In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. OBJECTIVE: In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. METHODS: We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. RESULTS: The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. CONCLUSIONS: Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

Correction: Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

[This corrects the article DOI: 10.2196/18585.].

Orthopaedic Surgery in Dystonic Cerebral Palsy.

BACKGROUND: Outcomes after orthopaedic interventions in patients with dystonic cerebral palsy (DCP) are historically regarded as unpredictable. This study aims to evaluate the overall outcome of orthopaedic surgery in children with DCP. METHOD: Children with DCP who underwent lower limb orthopaedic surgery with a minimum follow-up of 12 months were included. Data collected included age at time of surgery, surgical procedures performed, Gross Motor Function Classification System (GMFCS) level, and Barry Albright Dystonia Scale (BADS) score. The cohort was divided into 2 groups. Group 1 (GMFCS levels I to III), mean age 12 years 7 months and group 2 (GMFCS levels IV to V), mean age 10 years 7 months. Group 1 had surgery aimed at deformity correction to improve gait and mobility, and group 2 for the management or prevention of hip displacement. Outcome measures analyzed were: the incidence of unpredictable results related to surgery and early recurrence of deformity in both groups. Functional mobility scale scores were evaluated for group 1 and hip migration percentage for group 2. Linear mixed models were used to take into account repeated measures over time and correlations between measurements from the same patient. RESULTS: Group 1 (n=18); had low BADS scores and were considered to have mild dystonia. Three children experienced unpredictable results, 2 had early recurrence of deformity, 3 had a decline, and 1 child improved in the functional mobility scale.Group 2 (n=19); had high BADS scores and were considered to have moderate to severe dystonia. Nine surgical events involved bony procedures and 15 were soft tissue surgery only. One surgical event lead to unpredictable results and 2 children had early recurrence of deformity. Postoperatively, a linear trend of increasing migration percentage [0.49% (95% confidence interval, 0.23-0.74; P=0.0002)] was seen up to 21 months. There was no significant change after 21 months [-0.08% (95% confidence interval, -0.24 to +0.041; P=0.18)]. CONCLUSIONS: This study suggests that unpredictable results and early recurrence of deformity following orthopaedic surgery in children with DCP are not as common as previously regarded. Furthermore, functional mobility and hip morphology can be improved. LEVEL OF EVIDENCE: Level IV-this is a case-series.

Functional Impact of the N-terminal Arm of Proline Dehydrogenase from Thermus thermophilus.

Proline dehydrogenase (ProDH) is a ubiquitous flavoenzyme that catalyzes the oxidation of proline to Δ¹-pyrroline-5-carboxylate. Thermus thermophilus ProDH (TtProDH) contains in addition to its flavin-binding domain an N-terminal arm, consisting of helices αA, αB, and αC. Here, we report the biochemical properties of the helical arm truncated TtProDH variants ΔA, ΔAB, and ΔABC, produced with maltose-binding protein as solubility tag. All three truncated variants show similar spectral properties as TtProDH, indicative of a conserved flavin-binding pocket. ΔA and ΔAB are highly active tetramers that rapidly react with the suicide inhibitor N-propargylglycine. Removal of the entire N-terminal arm (ΔABC) results in barely active dimers that are incapable of forming a flavin adduct with N-propargylglycine. Characterization of V32D, Y35F, and V36D variants of ΔAB established that a hydrophobic patch between helix αC and helix α8 is critical for TtProDH catalysis and tetramer stabilization.