Examining the relationship between COVID-19 and suicide in media coverage through Natural Language Processing analysis

Background and objectives Suicide is a major public health concern, media can influence its awareness, contagion, and prevention. In this study, we evaluated the relationship between the COVID-19 pandemic and suicide in media coverage through Natural Language Processing analysis (NPL). Methods To study how suicide is depicted in news media, Artificial Intelligence and Big Data techniques were used to analyze news and tweets, to extract or classify the topic to which they belonged. Results A granger causality analysis showed with significant p-value that an increase in covid news at the beginning of the pandemic explains a later rise in suicide-related news. An analysis based on correlation and structural causal models show a strong relationship between the appearance of subjects “health” and “covid”, and also between “covid” and “suicide”. Conclusions Our analysis also uncovers that the inclusion of suicide-related news in the category health has grown since the outbreak of the pandemic. The COVID-19 pandemic has posed an inflection point in the way suicide-related news are reported. Our study found that the increased media attention on suicide during the COVID-19 pandemic may indicate rising social awareness of suicide and mental health, which could lead to the development of new prevention tools. (AU)

Flexiplex: a versatile demultiplexer and search tool for omics data.

MOTIVATION: The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks, such as identifying cellular barcodes and UMIs in single-cell data, and specific genetic variants for genotyping. However, existing tools, which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data. RESULTS: To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell-line-specific sequences in Illumina short-read single-cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single-cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools. AVAILABILITY AND IMPLEMENTATION: Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.

Neural Activity and Information Processing Capacity of Neuronal Culture.

Whether artificial or living, neural networks perform tremendously diverse kinds of information processing, such as visual feature extraction, speech recognition, motor control, and so on. Some studies have evaluated the computational ability of living neural networks based on the performances of specific tasks, yet could not comprehensively grasp the versatile ability. In this study, we investigated dissociated culture of neurons as a physical reservoir, which generates diverse outputs through linear regression, or readout, of the dynamical states. Based on the theory of reservoir computing, the potential computational capabilities of neuronal culture were evaluated by the information processing capacity (IPC), which indicates how a target function can be achieved from the given dynamics. As a result, we found that the neuronal culture exhibited significant IPC and that IPC varied with the inter-step interval (ISI), the time step of reservoir computing. The cultures exhibited a memory capacity of 10 time steps for computation, and this memory capacity decayed at an ISI of 5 ms or shorter. In addition, the IPC had a significant positive correlation with the intensity of the evoked response relative to spontaneous activity. The multiple regression model with evoked response and ISI showed the positive effect of evoked response and 30 ms as the best ISI for IPC. These results suggest that the distinct evoked response and the optimal time step to interact with the neuronal culture are key factors to uncover computational resources from the neuronal system.

The microbiology of sepsis is more than the application of new technologies in diagnosis

Adequate and rapid microbiological diagnosis of sepsis is essential for correct treatment, having a direct impact on patient prognosis. Clinical Microbiology Services must adapt fast circuits that allow prioritizing and individualizing the diagnosis of these patients. The measures adopted should not be based solely on the incorporation of new technologies but, to a large extent, on ensuring accurately collection and processing of samples, avoiding unnecessary losses of time in processing and ensuring that the information derived from this process adequately reaches the prescribing physician. (AU)

Lei Geral de Proteção de Dados e o controle social da saúde / General Data Protection Law and the social control of health

A proteção dos dados pessoais é um tema crucial para o controle social da saúde nesses tempos de capitalismo de vigilância em que há uma troca constante da privacidade dos indivíduos por serviços. Os capítulos dessa publicação são fruto de autores especialistas e convidados que participaram do Seminário online e gratuito intitulado "LGPD na Saúde: o CNS como articulador dos interesses da sociedade brasileira em Defesa da Vida", realizado em 2021. O evento foi promovido pelo Conselho Nacional de Saúde e nove mesas redondas conformaram três eixos de discussão: Acesso Universal à Saúde na Sociedade da Informação; Governo, Transformação Digital, Cidadania e o Controle Social da Saúde e Aspectos da Saúde Digital e da Ética em Pesquisa à Luz da LGPD. Enfim, esta coletânea visa contribuir com o cenário da governança das informações em saúde e a literacia dos atores do controle social na transição digital da saúde, suas práticas e tecnologias emergentes associadas.

How missing value imputation is confounded with batch effects and what you can do about it.

In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.

Molecular information technology with DNA.

Qian and Winfree constructed complex biochemical circuits with computation capability from scratch, demonstrating the programmability of biomolecules. One day, programming molecular information processing may be just like how electronic machines are programmed today, with exciting applications in nanoscale science and biotechnology.

Implementation of barcode medication administration (BMCA) technology on infusion pumps in the operating rooms.

BACKGROUND: Medication administration errors (MAEs) are a major cause of morbidity and mortality. An updated barcode medication administration (BCMA) technology on infusion pumps is implemented in the operating rooms to automate double check at a syringe exchange. OBJECTIVE: The aim of this mixed-methods before-and-after study is to understand the medication administrating process and assess the compliance with double check before and after implementation. METHODS: Reported MAEs from 2019 to October 2021 were analysed and categorised to the three moments of medication administration: (1) bolus induction, (2) infusion pump start-up and (3) changing an empty syringe. Interviews were conducted to understand the medication administration process with functional resonance analysis method (FRAM). Double check was observed in the operating rooms before and after implementation. MAEs up to December 2022 were used for a run chart. RESULTS: Analysis of MAEs showed that 70.9% occurred when changing an empty syringe. 90.0% of MAEs were deemed to be preventable with the use of the new BCMA technology. The FRAM model showed the extent of variation to double check by coworker or BCMA.Observations showed that the double check for pump start-up changed from 70.2% to 78.7% postimplementation (p=0.41). The BCMA double check contribution for pump start-up increased from 15.3% to 45.8% (p=0.0013). The double check for changing an empty syringe increased from 14.3% to 85.0% (p<0.0001) postimplementation. BCMA technology was new for changing an empty syringe and was used in 63.5% of administrations. MAEs for moments 2 and 3 were significantly reduced (p=0.0075) after implementation in the operating rooms and ICU. CONCLUSION: An updated BCMA technology contributes to a higher double check compliance and MAE reduction, especially when changing an empty syringe. BCMA technology has the potential to decrease MAEs if adherence is high enough.

"Are we there yet?" Ten persistent hazards and inefficiencies with the use of medication administration technology from the perspective of practicing nurses.

OBJECTIVES: (1) Characterize persistent hazards and inefficiencies in inpatient medication administration; (2) Explore cognitive attributes of medication administration tasks; and (3) Discuss strategies to reduce medication administration technology-related hazards. MATERIALS AND METHODS: Interviews were conducted with 32 nurses practicing at 2 urban, eastern and western US health systems. Qualitative analysis using inductive and deductive coding included consensus discussion, iterative review, and coding structure revision. We abstracted hazards and inefficiencies through the lens of risks to patient safety and the cognitive perception-action cycle (PAC). RESULTS: Persistent safety hazards and inefficiencies related to MAT organized around the PAC cycle included: (1) Compatibility constraints create information silos; (2) Missing action cues; (3) Intermittent communication flow between safety monitoring systems and nurses; (4) Occlusion of important alerts by other, less helpful alerts; (5) Dispersed information: Information required for tasks is not collocated; (6) Inconsistent data organization: Mismatch of the display and the user's mental model; (7) Hidden medication administration technologies (MAT) limitations: Inaccurate beliefs about MAT functionality contribute to overreliance on the technology; (8) Software rigidity causes workarounds; (9) Cumbersome dependencies between technology and the physical environment; and (10) Technology breakdowns require adaptive actions. DISCUSSION: Errors might persist in medication administration despite successful Bar Code Medication Administration and Electronic Medication Administration Record deployment for reducing errors. Opportunities to improve MAT require a deeper understanding of high-level reasoning in medication administration, including control over the information space, collaboration tools, and decision support. CONCLUSION: Future medication administration technology should consider a deeper understanding of nursing knowledge work for medication administration.

Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis.

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.

Barcode Medication Administration Technology to Prevent Medication Errors.

Medication errors cause harm to patients at any point along the medication administration process and can be prevented. Barcoding medication administration (BCMA) is effective as a clinical decision support system (CDSS) to avoid errors. This viewpoint proposes the implementation of BCMA to avoid potential adverse events. The opinion piece gives an overview of BCMA, reviews the current literature on its effectiveness, and sheds light on the associated challenges and how to overcome them. The objective of this article is to increase awareness regarding BCMA and how it can decrease patient morbidity and mortality, enhance safety, and lower overall hospital-associated costs by preventing medication errors. Key Words: Bar-code medication administration, Medication errors, Adverse drug events, Patient safety.

Interdisciplinary and Innovative: A Nursing and Computer Science Collaboration to Create a Barcode Medication Administration System.

ABSTRACT: Preventing medication errors remains a priority in nursing education. The implementation of Barcode Medication Administration (BCMA) systems is one strategy that has been used to reduce medication errors. Practice using BCMA in simulated settings may enhance the transfer of these skills to the clinical practice setting. However, the purchase of BCMA educational products available for nursing students can be cost prohibitive for many nursing programs. To overcome the barrier of cost, an interdisciplinary and innovative collaborative approach was used to create a fully functional low-cost BCMA system.

Improving Administration and Documentation of Enteral Nutrition Support Therapy in a Veteran Affairs Health Care System: Use of Medication Administration Record and Bar Code Scanning Technology.

OBJECTIVES: The goal of this project was to evaluate and improve the ordering, administration, documentation, and monitoring of enteral nutrition therapies within the inpatient setting in a Veteran's Health Administration system. METHODS: An interdisciplinary team of clinicians reviewed the literature for best practices and revised the process for enteral nutrition support for hospitalized veterans. Interventions included training staff, revising workflows to include scanning patients and products, including enteral nutrition orders within the medication administration record (MAR), and using the existing bar code medication administration system for administration, documentation, and monitoring. Baseline and postprocess improvement outcomes over a year period were collected and analyzed for quality improvement opportunities. RESULTS: Before process change, only 60% (33/55) of reviewed enteral nutrition orders were documented and 40% (22/55) were not documented in the intake flowsheet of the electronic health record. In the year after adding enteral nutrition therapies to the MAR and using bar code scanning, a total of 3807 enteral nutrition products were evaluated. One hundred percent of patients were bar code scanned, 3106/3807 (82%) products were documented as given, 447/3807 (12%) were documented as held (with comments), 12/3807 (<1%) were documented as missing/unavailable, and 242/3807 (6%) were documented as refused. CONCLUSIONS: Inclusion of enteral nutrition order sets on the MAR and using bar code scanning technology resulted in sustained improvements in safety, administration, and documentation of enteral therapies for hospitalized veterans.

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload.

OBJECTIVE: The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow. METHODS: Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time. RESULTS: As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2 = 0.237; marginal R2 = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs. CONCLUSION: Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.