Serial Data Research Articles

Estimating Reference Change Values Using Routine Patient Data: A Novel Pathology Database Approach.

The reference change value (RCV) is calculated by combining the within-subject biological variation (CVI) and local analytical variation (CVA). These calculations do not account for the variation seen in preanalytical conditions in routine practice or CVI in patients presenting for treatment. As a result, the RCVs may not reflect routine practice or align with clinicians' experiences. We propose a novel RCV approach based on routine patient data that is potentially more clinically relevant. This study used the refineR algorithm to determine RCVs using serial patient data extracted from a local Laboratory Information System (LIS). The model was applied to biomarkers with a range of result ratio distributions varying from normal to log-normal. Results were compared against conventional formula-based RCVs using CVI estimates from a state-of-the-art biological variation study. Monte Carlo simulations were also used to validate the LIS data approach. The RCVs estimated from LIS data were: 11-deoxycortisol (men): -70%/+196%, 17-hydroxyprogesterone (men): -49%/+100%, albumin: -10%/+11%, androstenedione (men): -47%/+96%, cortisol (men): -54%/+51%, cortisone (men): -32%/+51%, creatinine: -16%/+14%, phosphate (women): -23%/+29%, phosphate (men): -27%/+29%, testosterone (men): -38%/+60%. The formula-based RCV estimates showed similar but slightly lower results, and the Monte Carlo simulations confirmed the applicability of the new approach. RCVs may be estimated from patient results without prior assumptions about the shape of the ratios between serial results. Laboratories can determine RCVs based on local practice and population.

Temporal and spatial trends of fentanyl co-occurrence in the illicit drug supply in the United States: a serial cross-sectional analysis

BackgroundFentanyl and its analogs contribute substantially to drug overdose deaths in the United States. There is concern that people using drugs are being unknowingly exposed to fentanyl, increasing their risk of overdose death. This study examines temporal trends and spatial variations in the co-occurrence of fentanyl with other seized drugs. MethodsWe identified fentanyl co-occurrence (the proportion of samples of non-fentanyl substances that also contain fentanyl) among 9 substances or substance classes of interest: methamphetamine, cannabis, cocaine, heroin, club drugs, hallucinogens, and prescription opioids, stimulants, and benzodiazepines. We used serial cross-sectional data on drug reports across 50 states and the District of Columbia from the National Forensic Laboratory Information System, the largest available database on the U.S. illicit drug supply, from January 2013 to December 2023. FindingsWe analyzed data from 11,940,207 samples. Fentanyl co-occurrence with all examined substances increased monotonically over time (Mann-Kendall p<0.0001). Nationally, fentanyl co-occurrence was highest among heroin samples (approx. 50%), but relatively low among methamphetamine (≤1%), cocaine (≤4%), and other drug samples. However, co-occurrence rates have grown to over 10% for cocaine and methamphetamine in several Northeast states in 2017-2023. InterpretationFentanyl co-occurs most commonly with heroin, but its presence in stimulant supplies is increasing in some areas, where it may pose a disproportionately high risk of overdose. FundingThis work was partly supported by FDA grant U01FD00745501. This article reflects the views of the authors and does not represent the views or policies of the FDA or US Department of Health and Human Services.

Crystal structure of a bacterial photoactivated adenylate cyclase determined by serial femtosecond and serial synchrotron crystallography.

OaPAC is a recently discovered blue-light-using flavin adenosine dinucleotide (BLUF) photoactivated adenylate cyclase from the cyanobacterium Oscillatoria acuminata that uses adenosine triphosphate and translates the light signal into the production of cyclic adenosine monophosphate. Here, we report crystal structures of the enzyme in the absence of its natural substrate determined from room-temperature serial crystallography data collected at both an X-ray free-electron laser and a synchrotron, and we compare these structures with cryo-macromolecular crystallography structures obtained at a synchrotron by us and others. These results reveal slight differences in the structure of the enzyme due to data collection at different temperatures and X-ray sources. We further investigate the effect of the Y6W mutation in the BLUF domain, a mutation which results in a rearrangement of the hydrogen-bond network around the flavin and a notable rotation of the side chain of the critical Gln48 residue. These studies pave the way for picosecond-millisecond time-resolved serial crystallography experiments at X-ray free-electron lasers and synchrotrons in order to determine the early structural intermediates and correlate them with the well studied picosecond-millisecond spectroscopic intermediates.

Effect of alirocumab on coronary plaque stratified by atherothrombotic risks

Abstract Background Previous clinical studies have shown that high-risk subgroups including patients with major atherosclerotic cardiovascular disease (ASCVD) events and multiple atherothrombotic risk factors derive enhanced cardiovascular benefit from proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition. To date, the effect of alirocumab on coronary plaque regression stratified by the number of atherothrombotic risk factors remains unknown. Purpose To investigate the utility of atherothrombotic risk stratification to identify patients with acute myocardial infarction (AMI) who have the greatest potential for benefit on plaque regression as assessed by intracoronary imaging from the addition of alirocumab to high-intensity statin therapy. Methods This was a substudy of the PACMAN trial, a randomized, double-blind trial comparing biweekly alirocumab (150 mg) versus placebo in AMI patients undergoing percutaneous coronary intervention. Intravascular ultrasound (IVUS) was serially performed in non-infarct-related coronary arteries at baseline and after 52 weeks. Atherothrombotic risk factors defined in the 2018 ACC/AHA cholesterol guidelines include myocardial infarction, ischemic stroke, peripheral artery disease, age ≥65 years, familial hypercholesterolemia, coronary revascularization, diabetes, hypertension, chronic kidney disease, current smoking, LDL-C ≥100 mg/dL despite maximum tolerated statin therapy, and history of heart failure. The key endpoint was the change in IVUS-derived percent atheroma volume (PAV) from baseline to 52 weeks. Results A total of 263 patients available for serial IVUS data were analyzed for the current study. The most prevalent atherothrombotic risk factors were current smoking (49%), hypertension (43%), and age ≥65 years (24%). Patients were divided into 3 groups according to the number of risk factors (0 risk factor: n=52, 1 risk factor: n=101, ≥2 risk factors: n=100). Patients with risk factors had greater PAV at baseline compared with those with 0 risk factor (39.0% vs. 42.6% vs. 42.8%, P=0.008). The addition of Alirocumab to high-intensity statin therapy demonstrated a significant reduction in PAV among patients with 0 risk factor (-2.66% vs. -1.02%, P=0.009) and those with 1 risk factor (-2.36% vs. -0.76%, P&amp;lt;0.001) (Figure 1). In contrast, patients with ≥2 risk factors had no significant PAV reduction by alirocumab (-1.72% vs. -1.12%, P=0.076, P for interaction=0.009). Conclusions Among AMI patients with no or 1 atherothrombotic risk factor, the addition of alirocumab to high-intensity statin therapy demonstrated greater coronary plaque regression. Patients with fewer risk factors thus appear to be more susceptible for atheroma regression.Change in PAV stratified by risk factors

Delineating three distinct spatiotemporal patterns of brain atrophy in Parkinson's disease.

The clinical manifestation of Parkinson's disease exhibits significant heterogeneity in the prevalence of non-motor symptoms and the rate of progression of motor symptoms, suggesting that Parkinson's disease can be classified into distinct subtypes. In this study, we aimed to explore this heterogeneity by identifying a set of subtypes with distinct patterns of spatiotemporal trajectories of neurodegeneration. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that combined disease progression modelling with clustering methods, to cortical and subcortical neurodegeneration visible on 3 T structural MRI of a large cross-sectional sample of 504 patients and 279 healthy controls. Serial longitudinal data were available for a subset of 178 patients at the 2-year follow-up and for 140 patients at the 4-year follow-up. In a subset of 210 patients, concomitant Alzheimer's disease pathology was assessed by evaluating amyloid-β concentrations in the CSF or via the amyloid-specific radiotracer 18F-flutemetamol with PET. The SuStaIn analysis revealed three distinct subtypes, each characterized by unique patterns of spatiotemporal evolution of brain atrophy: neocortical, limbic and brainstem. In the neocortical subtype, a reduction in brain volume occurred in the frontal and parietal cortices in the earliest disease stage and progressed across the entire neocortex during the early stage, although with relative sparing of the striatum, pallidum, accumbens area and brainstem. The limbic subtype represented comparative regional vulnerability, which was characterized by early volume loss in the amygdala, accumbens area, striatum and temporal cortex, subsequently spreading to the parietal and frontal cortices across disease stage. The brainstem subtype showed gradual rostral progression from the brainstem extending to the amygdala and hippocampus, followed by the temporal and other cortices. Longitudinal MRI data confirmed that 77.8% of participants at the 2-year follow-up and 84.0% at the 4-year follow-up were assigned to subtypes consistent with estimates from the cross-sectional data. This three-subtype model aligned with empirically proposed subtypes based on age at onset, because the neocortical subtype demonstrated characteristics similar to those found in the old-onset phenotype, including older onset and cognitive decline symptoms (P < 0.05). Moreover, the subtypes correspond to the three categories of the neuropathological consensus criteria for symptomatic patients with Lewy pathology, proposing neocortex-, limbic- and brainstem-predominant patterns as different subgroups of α-synuclein distributions. Among the subtypes, the prevalence of biomarker evidence of amyloid-β pathology was comparable. Upon validation, the subtype model might be applied to individual cases, potentially serving as a biomarker to track disease progression and predict temporal evolution.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

Asian representation in the United States (US) oncology workforce.

152 Background: The American Society of Clinical Oncology (ASCO) started the Diversity in Oncology initiative in 2017. Minority representation in the US oncology physician workforce was the subject of a recent series of ASCO State of the Oncology Workforce in America snapshots (1). However, Asian minority representation was not mentioned in any of these reports. Our objective was to determine the proportion of Asian workforce in various oncology specialties inclusive of physician and non-physician practitioners as well as physician trainees. Methods: We searched the web for publicly available data on Asian representation among US oncology adult (A) and pediatric (P) practitioners and trainees. Major data sources included the 2020 US Census, Association of American Medical Colleges, Accreditation Council for Graduate Medical Education, and professional societies. Practitioners included hematologists/oncologists (H/O), gynecologic oncologists (GYNO), radiation oncologists (RADO), surgical oncologists (SURGO), hematopathologists (HP), oncology nurse practitioners (ONP), oncology physician assistants (OPA), and oncology nurses (ON). Trainees included fellows (F) and residents (R). For reference, we obtained similar data on US population, medical students (MS), adult internal medicine residents (AIMR), gynecology residents (GYNR), pediatric residents (PR), pathology residents (PATHR), surgery residents (SURGR), nurses (RN), nurse practitioners (NP), and physician assistants (PA). For a snapshot of the oncology workforce, data from 2020-2022 were used. Whenever available, we obtained serial data for trainees over the years. Results: A snapshot of the proportions of Asians based on their professions is shown (Table). While Asians comprise only 6.2% of the US population, between 14.7% to 32.1% of physician practitioners in various oncology subspecialties are Asian. Moreover, between 17.9% to 35.3% of oncology physician trainees and 26.7% of medical students are Asian and these proportions have remained stable from 2005 to 2022. Data are unavailable for HP, SURGO, and non-physician practitioners except for OPA (7.0%) and RN (7.2%). Conclusions: Asians comprise a large segment of the US oncology workforce. While only 1 in 20 people in the US are Asians, between 1 in 3 and 1 in 5 oncology physician practitioners and trainees are Asians. Data are limited for non-physician practitioners. Asian minority representation should be highlighted and considered as an example of success in diversity. 1. JCO Oncol Pract 2020-2022. Snapshot of Asian representation among oncology trainees and practitioners in the US. Trainees 2022 A-H/O-F 35.3% AIMR 35.9% 2022 P-H/O-F 20.0% PR 22.0% 2022 RADO-R 26.4% MS 26.7% 2022 GYNO-F 17.9% GYNR 14.9% 2022 HP-F 20.0% PATHR 26.6% 2022 SURGO-F 21.2% SURGR 19.2% Practitioners 2021 A-H/O 32.1% OPA 7.0% 2021 P-H/O 20.4% RN 7.2% 2021 RADO 25.9% HP/SURGO/ONP/ON NA 2020 GYNO 14.7% US Census 6.2%

Predictive value of serum interleukin-6 for neonatal encephalopathy outcomes.

Serum interleukin-6 (IL-6) may predict adverse outcomes of neonatal encephalopathy (NE); however, limited data regarding the predictive utility of IL-6 during neurodevelopmental follow-up are available. We aimed to determine the utility of IL-6 for predicting adverse outcomes at 18 to 22 months of age. Eighty-seven patients with NE who received therapeutic hypothermia were enrolled in this study. Serial serum IL-6 levels during the first 3 postnatal days were collected. Patients were classified into three groups: 1) death, 2) survival with moderate to severe neurodevelopmental disability (NDD) at 18-22 months of age, and 3) survival without NDD (favorable outcome). The predictive ability of IL-6 was determined by the area under the receiver-operating characteristic curve (AUC). Serial IL-6 data of 80 patients with NE were available and showed peak levels on postnatal day 1; these levels gradually decreased toward day 3. By 18-22 months of age, 13 and 17 patients died and experienced moderate to severe NDD without death, respectively. Fifty patients experienced favorable outcomes. Higher IL-6 levels on day 1 predicted the composite adverse outcome (including death and survival with NDD; n = 30; AUC, 0.648). Higher IL-6 levels on day 1 predicted death (n = 13; AUC, 0.799), whereas higher IL-6 levels on day 1 predicted survival with NDD (n = 17; AUC, 0.536). The AUC of IL-6 that predicted survival with NDD was lower than the AUC of IL-6 that predicted death; therefore, IL-6 may have insufficient utility for predicting NDD without death.

National Trends in the Utilization and Expenditure of Chiropractic Care in U.S. Adults: Analysis of the 2007-2016 Medical Expenditure Panel Survey

ObjectiveThis study aimed to examine the trends in the utilization and expenditure of chiropractic care in a representative sample of US adults, aged ≥18 years. MethodsSerial cross-sectional data (2007-2016) from the Medical Expenditure Panel Survey (MEPS) were examined. Weighted descriptive statistics were analyzed to obtain national estimates of chiropractic utilization and expenditure, and time-series linear regression was used to assess trends over time. Socio-demographic characteristics and musculoskeletal diagnoses associated with chiropractic use were also reported. ResultsA statistically significantly increasing trend was observed for the number of adults receiving chiropractic care (p < .05), number of visits (p < .05) and utilization rate (P < .05) from 2007 to 2016. A similar trend was not found for chiropractic expenditure during this period (P > .05). The mean number of visits was 8.3 visits per year, with a mean expenditure of $86.94 USD per visit and $721.43 USD per person per year. Mean age of adult chiropractic users each year ranged between 48.6 and 51.2 years old, and users were primarily female (56.3%-60.4%), White persons (90.1%-93.5%), and privately insured (77.3%-82.8%). The most prevalent musculoskeletal diagnoses associated with chiropractic use were low back conditions (45.4%-58.1%), inflammatory/degenerative disc or joint conditions (21.2%-26.8%) and head and neck complaints (9.8%-13.7%). ConclusionThe findings showed statistically significant increasing trends for the number of adult chiropractic users, the number of visits, and percent utilization of chiropractic care from 2007 to 2016. Conversely, we found a statistically null trend for the total annual expenditure on chiropractic care during the same time period. Findings from this analysis imply that, while chiropractic care appears to be growing, it may still be under-utilized, and more efforts should be devoted to ensuring sustained growth and a larger role in the management of musculoskeletal health.

Beyond neurons: computer vision methods for analysis of morphologically complex astrocytes

The study of the geometric organization of biological tissues has a rich history in the literature. However, the geometry and architecture of individual cells within tissues has traditionally relied upon manual or indirect measures of shape. Such rudimentary measures are largely a result of challenges associated with acquiring high resolution images of cells and cellular components, as well as a lack of computational approaches to analyze large volumes of high-resolution data. This is especially true with brain tissue, which is composed of a complex array of cells. Here we review computational tools that have been applied to unravel the cellular nanoarchitecture of astrocytes, a type of brain cell that is increasingly being shown to be essential for brain function. Astrocytes are among the most structurally complex and functionally diverse cells in the mammalian body and are essential partner cells of neurons. Light microscopy does not allow adequate resolution of astrocyte morphology, however, large-scale serial electron microscopy data, which provides nanometer resolution 3D models, is enabling the visualization of the fine, convoluted structure of astrocytes. Application of computer vision methods to the resulting nanoscale 3D models is helping reveal the geometry and organizing principles of astrocytes, but a complete understanding of astrocyte structure and its functional implications will require further adaptation of existing computational tools, as well as development of new approaches.

Changes in unhealthy food consumption among vulnerable elementary-aged children in the United States during the COVID-19 pandemic: a serial cross-sectional analysis.

Several studies suggest that during the early pandemic, amidst socioeconomic instability, children from underserved families were more likely to resort to consuming cheaper, lower-quality foods with longer shelf lives. This study investigated the change in unhealthy food consumption across different phases (pre, early, mid) of the COVID-19 pandemic, and whether the strength of association between unhealthy food consumption and household socioeconomic disadvantage (HSED) varied across phases of the pandemic. This study utilized serial cross-sectional data collected from low-income families enrolled in a school-based food co-op, Brighter Bites. Secondary data analysis included 5,384 surveys from families who had complete data: 3422 pre-pandemic, 944 from the early pandemic, and 1,018 mid-pandemic. Outcome measures included sugary food intake and convenience/fast food intake, each of which was operationalized as a scale using pre-validated items from the surveys. HSED was operationalized as a composite of parent employment, parent education, food insecurity, and participation in government programs; responses were categorized into low, medium, and high HSED groups for analysis. We examined the interaction between HSED and time period to explore how HSED and its association with dietary measures changed over the course of the pandemic. A significant linear negative trend, i.e., a decrease in consumption from pre-to-mid-pandemic period was seen in sugary food intake (p < 0.001), but not in convenience food intake. In data pooled across time-periods, both sugary food and convenience/fast food consumption were inversely associated with HSED, and low unhealthy food consumption was observed among high-disadvantage groups. No significant interaction between time period and HSED was observed on either scale. However, the post-regression estimates from the adjusted interaction model showed a significant association between convenience/fast food consumption and HSED in pre- and mid-pandemic periods. The study highlights the nuances of socioeconomic dynamics on the diet behaviors of children from low-income families during a natural disaster.

Implications of Global Lung Function Initiative Spirometry Reference Equations in Northeast Asian Patients With COPD

Accurate spirometry interpretation is critical in the diagnosis and management of chronic obstructive pulmonary disease (COPD). With increasing efforts for a unified approach by the Global Lung Function Initiative (GLI), this study evaluated the application of race-specific 2012-GLI and race-neutral 2022-GLI reference equations compared to Choi's reference equations, which is derived and widely used in South Korea, for spirometry interpretation in Northeast Asian patients with COPD. What are the effects of applying race-specific 2012-GLI, race-neutral 2022-GLI, and Choi's reference equations on the diagnosis, severity grade, and clinical outcome associations of COPD STUDY DESIGN AND METHODS: Serial spirometry data from the Korea COPD Subgroup Study (KOCOSS) consisting of 3,477 patients were utilized for re-analyses using 2012-GLI, 2022-GLI, and Choi's reference equations. The COPD diagnosis and severity categorization, associations with disease manifestations and health outcomes, and longitudinal trajectories of lung function were determined. Although there was strong concordance in COPD diagnosis comparing 2012-GLI, and 2022-GLI reference equations to Choi's reference equations, a notable portion of patients were reclassified to milder disease severity (17.0% and 23.4% for 2012-GLI and 2022-GLI reference equations, respectively). Relationships between FEV1 percent-predicted values calculated using 2012-GLI, 2022-GLI, and Choi's equations with clinical outcomes including dyspnea severity, exercise capacity, health-related quality of life, and frequency of exacerbations remain consistently significant. Similar annual decline rates of FEV1 and FVC percent-predicted were observed among the reference equations used, except for slower annual decline rate of FEV1 in Choi's equation compared to 2022-GLI race-neutral equation. Application of GLI reference equations for spirometry interpretation in Northeast Asian patients with COPD has potential implications on disease severity grade for clinical management and trial participation, and maintains consistent significant relationships with key disease outcomes.

Development of a true density-based automated quality grading device for unboiled arecanut kernels

The automated sorting of arecanut kernels is a significant challenge that has not been effectively addressed thus far. Scientific grading techniques are necessary given the paradigm change toward investigating alternative uses for arecanuts in industry and the medical field. This research work emphasizes the relatively unexplored aspect of the post-harvest process; quality grading of kernels based on physical properties. It aimed to develop a novel approach for classifying unboiled (Chali) arecanut kernels cultivated in Goa, India based on their true density, using a combination of mechanical and visual techniques. The study explored the potential of true density as a quality indicator for real-time grading of the kernels. To achieve this, automated grading equipment was devised, utilizing a load cell to measure the kernel's mass and the ellipsoid approximation method to estimate its volume. A machine vision system captured the top and side images of the kernels to measure their volume. Python programs were created to enable image acquisition, processing, object detection, measurement and kernel segregation. Real-time kernel classification was accomplished by establishing serial data communication between the Python code and the Arduino board. The kernel segregation process was facilitated by servomechanism and a stepper motor. The kernels were classified into acceptable and non-acceptable categories based on a threshold value of true density. The research successfully established a method that utilizes the physical attributes of arecanut kernels as parameters for quality grading. However, the study encountered challenges with the density measurements, as the paired t-test results revealed significant differences between the kernel true density measured by the device and the true density estimated using the weighing scale-water displacement method, indicating a percentage error of 13.2%. Addressing these challenges would lead to more accurate density calculations, thereby enhancing the overall effectiveness of the kernel classification process. Furthermore, the technique allowed for the offline estimation of the kernels' porosity, which was found to be 45.3%. In future research, the integration of density and porosity measurement systems could be explored for real-time quality evaluation based on porosity, offering potential opportunities for further enhancement and optimization of the grading process. The technology could be further applied to other types of nuts and agricultural products, thereby overcoming the limitations of color-based sorting using image processing. Key words: Quality grading, True density, Machine vision, Arecanut kernel, Python

Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning

AbstractBackgroundShock is a life‐threatening condition that is associated with high mortality and morbidity. Therefore, the timely identification and management of this condition are important. We aimed to develop a prediction model for vasopressor use based on concise serial arterial blood pressure data.MethodsWe collected continuous arterial blood pressure from patients admitted to the emergency department (ED) resuscitation room. Patients with an initial systolic blood pressure lower than 90 mmHg were included in the study. We developed prediction models using convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks. Discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC).ResultsA total of 120 patients were enrolled in the study. The CNN and LSTM models yielded AUROCs ranging from 0.731 to 0.834 for predicting the need for vasopressor infusion within different time frames (30 min, 1 h, and 6 h). LSTM outperformed the CNN in terms of predicting vasopressor infusion within 30 min and 1 h (p value &lt; 0.05). The AUPRC values ranged from 0.200 to 0.252, and sensitivity–specificity analyses indicated that the models have potential for clinical applications.ConclusionThis study used serial arterial blood pressure data to construct a promising prediction model for the need for vasopressors in hypotensive ED patients. The simplicity and accuracy of the model provide valuable insights for developing a clinical decision support tool for resuscitation in emergency settings.

Epidemiological impact of revoking mask-wearing recommendation on COVID-19 transmission in Tokyo, Japan

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a real-time quantification of the effects of relaxing countermeasures.

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Abstract Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.

Increasing HIV Testing Among Sexual and Gender Expansive Men in Kazakhstan: A Stepped-Wedge Randomized Trial of a Community-Level Intervention.

HIV transmission in Kazakhstan has increased among men who have sex with men (MSM) and transgender and nonbinary people who have sex with men (TSM), driven by low HIV testing rates. To determine if the PRIDE in HIV Care intervention had a community effect of increasing HIV testing among MSM and TSM in Kazakhstan. We employed a stepped-wedge, cluster-randomized controlled trial with MSM and TSM community members recruited from three cities in Kazakhstan: Almaty, Astana, and Shymkent. We collected serial cross-sectional data where community members completed one assessment between 21 August 2018, and 30 March 2022. We collected data from 629 MSM and TSM among the study cities. Community respondents were recruited from real-world (e.g., NGOs, bars, clubs) or virtual sites (e.g., social media, apps) where MSM and TSM in each of the three cities were known to frequent. Eligibility criteria for community respondents were: (1) ≥18 years old; (2) identifying as male at any point in life or being assigned male at birth; (3) having consensual sex with another man in the past 12 months; (4) engaging in binge drinking (i.e., ≥5 drinks in a 2 hour period), illicit use of drugs, or both in the past 90 days; and (5) residing in one of the three study cities. The PRIDE in HIV Care intervention is a theory-driven "crowdsourcing and peer-actuated network intervention" designed to amplify community members' successes and resilience via "influencers" who can strengthen and impart benefit to their networks and community. Received an HIV test in the prior six months. There was a statistically significant increase in odds of recent HIV testing for every additional month the intervention was implemented in a respondent's city (AOR=1.08, 95% CI=1.05-1.12; p<.001). The PRIDE in HIV Care intervention appears to be efficacious in enacting a community wide increase-i.e., promoted HIV testing among those who did not go through the intervention itself-in HIV testing among MSM and TSM. This trial is registered with clinicaltrials.gov (NCT02786615). National Institute on Drug Abuse (NIDA), grant number R01DA040513.

Liver stiffness progression in biopsy-proven metabolic dysfunction-associated steatotic disease among people with diabetes versus people without diabetes: A prospective multicenter study.

There are limited data on the progression of liver stiffness measurement (LSM) by vibration-controlled transient elastography (VCTE) in people with type 2 diabetes mellitus (T2DM) versus those without T2DM in biopsy-proven metabolic dysfunction-associated steatotic liver disease. We examined LSM progression in participants with T2DM versus those without T2DM in a large, prospective, multicenter cohort study. This study included 1231 adult participants (62% female) with biopsy-proven metabolic dysfunction-associated steatotic liver disease who had VCTEs at least 1 year apart. LSM progression and regression were defined by a ≥20% increase and an upward or downward change, respectively, in the LSM category in the Baveno VII categories for compensated advanced chronic liver disease, compared between participants with T2DM (n = 680) versus no T2DM (n = 551) at baseline. The mean (±SD) age and body mass index were 51.8 (±12.0) years and 34.0 (±6.5) kg/m 2 , respectively. The median (IQR) time between the first and last VCTE measurements was 4.1 (2.5-6.5) years. Participants with T2DM had higher LSM progression at 4 years (12% vs. 10%), 6 years (23% vs. 16%), and 8 years (50% vs. 39%), p = 0.04. Using a multivariable Cox proportional hazards model adjusted for multiple confounders, the presence of T2DM remained an independent predictor of LSM progression (adjusted HR: 1.35, 95% CI: 1.01-1.81, p = 0.04). T2DM was not associated with LSM regression ( p = 0.71). Mean HbA1c was significantly associated with LSM progression ( p = 0.003) and regression ( p = 0.02). Using serial VCTE data from a multicenter study of participants with biopsy-proven metabolic dysfunction-associated steatotic liver disease, we demonstrate that T2DM and HbA1c are associated with LSM progression.

Correlative X-ray Computed Tomography and Optical Microscopy Serial Sectioning Data of Additive Manufactured Ti-6Al-4V

Correlative X-ray Computed Tomography and Optical Microscopy Serial Sectioning Data of Additive Manufactured Ti-6Al-4V

Appraising protein conformational changes by resampling time-resolved serial x-ray crystallography data.

With the development of serial crystallography at both x-ray free electron laser and synchrotron radiation sources, time-resolved x-ray crystallography is increasingly being applied to study conformational changes in macromolecules. A successful time-resolved serial crystallography study requires the growth of microcrystals, a mechanism for synchronized and homogeneous excitation of the reaction of interest within microcrystals, and tools for structural interpretation. Here, we utilize time-resolved serial femtosecond crystallography data collected from microcrystals of bacteriorhodopsin to compare results from partial occupancy structural refinement and refinement against extrapolated data. We illustrate the domain wherein the amplitude of refined conformational changes is inversely proportional to the activated state occupancy. We illustrate how resampling strategies allow coordinate uncertainty to be estimated and demonstrate that these two approaches to structural refinement agree within coordinate errors. We illustrate how singular value decomposition of a set of difference Fourier electron density maps calculated from resampled data can minimize phase bias in these maps, and we quantify residual densities for transient water molecules by analyzing difference Fourier and Polder omit maps from resampled data. We suggest that these tools may assist others in judging the confidence with which observed electron density differences may be interpreted as functionally important conformational changes.