Search

biBackground:/i/bThe microbial community in human milk is associated with many maternal and neonatal factors. This study aimed to investigate the effect of antibiotic exposure on the microbial community structure of colostrum.biMethods:/i/bTwenty women with antibiotic treatment immediately after delivery and 10 age-matched control women were enrolled at the Guangdong Women and Children Hospital. Colostrum samples were collected within postpartum 30 hours. The V4 variable region of the bacterial 16S rRNA gene was sequenced to characterize the microbial profile using Illumina MiSeq platform.biResults:/i/bPhylaiProteobacteria/iandiFirmicutes/iwere the predominant bacteria in colostrum samples. The core and abundant genera in colostrum includediStreptococcus/i,iStaphylococcus/i, andiPseudomonas/i. Compared with the control group, principal coordinate analysis based on the Bray-Curtis distance showed a significant difference in milk microbial community in women with antibiotic exposure, accompanied by a significantly lower alpha diversity and a different microbial ecological network. Furthermore, the relative abundances of generaiActinomyces/i,iAnaerobacter/i, andiClostridium_/isensui_stricto/isignificantly decreased after antibiotic treatment.biConclusions:/i/bThis study provided evidence of alterations in the colostrum microbial community with antibiotic exposure, improving our understanding of the effects of antibiotic treatment on the milk microbiome.

Purpose:Cancer treatments can paradoxically appear to reduce the risk of noncancer mortality in observational studies, due to residual confounding. Here we introduce a method, Bias Reduction through Analysis of Competing Events (BRACE), to reduce bias in the presence of residual confounding.Experimental Design:BRACE is a novel method for adjusting for bias from residual confounding in proportional hazards models. Using standard simulation methods, we compared BRACE with Cox proportional hazards regression in the presence of an unmeasured confounder. We examined estimator distributions, bias, mean squared error (MSE), and coverage probability. We then estimated treatment effects of high versus low intensity treatments in 36,630 prostate cancer, 4,069 lung cancer, and 7,117 head/neck cancer patients, using the Veterans Affairs database. We analyzed treatment effects on cancer-specific mortality (CSM), noncancer mortality (NCM), and overall survival (OS), using conventional multivariable Cox and propensity score (adjusted using inverse probability weighting) models, versus BRACE-adjusted estimates.Results:In simulations with residual confounding, BRACE uniformly reduced both bias and MSE. In the absence of bias, BRACE introduced bias toward the null, albeit with lower MSE. BRACE markedly improved coverage probability, but with a tendency toward overcorrection for effective but nontoxic treatments. For each clinical cohort, more intensive treatments were associated with significantly reduced hazards for CSM, NCM, and OS. BRACE attenuated OS estimates, yielding results more consistent with findings from randomized trials and meta-analyses.Conclusions:BRACE reduces bias and MSE when residual confounding is present and represents a novel approach to improve treatment effect estimation in nonrandomized studies.

Supplementary Data from Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data

Supplementary Figure from Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data

Supplementary Table from Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data

The use of patient-derived organoids (PDOs) to characterize therapeutic sensitivity and resistance (pharmacotyping) is a promising precision medicine approach. The potential of this approach to inform clinical decisions is now being tested in several large multi-institutional clinical trials. PDOs are cultivated in extracellular matrix from basement membrane extracts (BMEs) that are most commonly acquired commercially. Each clinical site utilizes distinct BME lots and may be restricted due to the availability of commercial BME sources. However, the impact of different sources and lots of BMEs on organoid drug response is unknown. Here, we tested the impact of BME source and lot on proliferation, chemotherapy and targeted therapy drug response, and gene expression in mouse and human pancreatic ductal adenocarcinoma (PDA) organoids. Both human and mouse organoids displayed increased proliferation in Matrigel (Corning) compared to Cultrex (RnD) and UltiMatrix (RnD). However, we observed no substantial impact on drug response when oragnoids were cultured in Matrigel, Cultrex, or UltiMatrix. We also did not observe major shifts in gene expression across the different BME sources, and PDOs maintained their Classical or Basal-like designation. Overall, we find that BME source (Matrigel, Cultrex, UltiMatrix) does not shift PDO dose-response curves and drug testing results, indicating that PDO pharmacotyping is a robust approach for precision medicine.

Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting nontumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating nontumor radiomics, including whole-body features, to predict treatment outcomes in patients with previously untreated locoregionally advanced cervical cancer. Methods: We analyzed 127 cervical cancer patients treated definitively with chemoradiotherapy and intracavitary brachytherapy. All patients underwent pretreatment whole-body 18F-FDG PET/CT. To quantify effects due to the tumor itself, the gross tumor volume (GTV) was directly contoured on the PET/CT image. Meanwhile, to quantify effects arising from the rest of the body, the planning target volume (PTV) was deformably registered from each planning CT to the PET/CT scan, and a semiautomated approach combining seed-growing and manual contour review generated whole-body muscle, bone, and fat segmentations on each PET/CT image. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. Ninety-five patients were used to train a Cox model of disease recurrence including both radiomic and clinical features (age, stage, tumor grade, histology, and baseline complete blood cell counts), using bagging and split-sample-validation for feature reduction and model selection. To further avoid overfitting, the resulting models were tested for generalization on the remaining 32 patients, by calculating a risk score based on Cox regression and evaluating the c-index (c-index > 0.5 indicates predictive power). Results: Optimal performance was seen in a Cox model including 1 clinical biomarker (whether or not a tumor was stage III-IVA), 2 GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), 1 PTV radiomic biomarker (major axis length), and 1 whole-body radiomic biomarker (CT bone root mean square). In particular, stratification into high- and low-risk groups, based on the linear risk score from this Cox model, resulted in a hazard ratio of 0.019 (95% CI, 0.004, 0.082), an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 (95% CI, 0.16, 0.83). Conclusion: Incorporating nontumor radiomic biomarkers can improve the performance of prognostic models compared with using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multiinstitutional cohorts.

Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines.

Pepsinogen I (PGI) can reflect the morphology and function of the gastric mucosa. Accordingly, the large-scale community health screening of PGI can dramatically increase the early diagnosis rate of gastric cancer. However, PGI testing can only be carried out in comprehensive hospitals and health examination centers. To ameliorate this issue, a point-of-care chemiluminescent immunoassay for PGI was developed in a fully automated miniaturized instrument. This instrument was especially developed for health check-ups in the grassroots communities; its volume of which is only 0.18 m3. Critically, the entire detection process for a single sample only requires 20 min, and the samples can be loaded continuously, making the method suitable for high-throughput analysis. The assay displayed an excellent detection limit of 0.048 ng/mL with a broad detection range of 0–200 ng/mL. Furthermore, this assay exhibited high sensitivity and specificity, had low intra- and inter-assay coefficients of variation (

Supplemental digital content is available in the text., Introduction Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method. Methods CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification). Results For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%–83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP’s positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min). Conclusion CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here, we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N = 10 145) of 9–11-year-old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.

Background Diffusion-weighted (DW) echo-planar imaging (EPI) is prone to geometric distortions due to B0 inhomogeneities. Both prospective and retrospective approaches have been developed to decrease and correct such distortions. Purpose The purpose of this work was to evaluate the performance of reduced-field-of-view (FOV) acquisition and retrospective distortion correction methods in decreasing distortion artifacts for breast imaging. Coverage of the axilla in reduced-FOV DW magnetic resonance imaging (MRI) and residual distortion were also assessed. Study type Retrospective. Population/phantom Breast phantom and 169 women (52.4 ± 13.4 years old) undergoing clinical breast MRI. Field strength/sequence A 3.0 T/ full- and reduced-FOV DW gradient-echo EPI sequence. Assessment Performance of reversed polarity gradient (RPG) and FSL topup in correcting breast full- and reduced-FOV EPI data was evaluated using the mutual information (MI) metric between EPI and anatomical images. Two independent breast radiologists determined if coverage on both EPI data sets was adequate to evaluate axillary nodes and identified residual nipple distortion artifacts. Statistical tests Two-way repeated-measures analyses of variance and post hoc tests were used to identify differences between EPI modality and distortion correction method. Generalized linear mixed effects models were used to evaluate differences in axillary coverage and residual nipple distortion. Results In a breast phantom, residual distortions were 0.16 ± 0.07 cm and 0.22 ± 0.13 cm in reduced- and full-FOV EPI with both methods, respectively. In patients, MI significantly increased after distortion correction of full-FOV (11 ± 5% and 18 ± 9%, RPG and topup) and reduced-FOV (8 ± 4% both) EPI data. Axillary nodes were observed in 99% and 69% of the cases in full- and reduced-FOV EPI images. Residual distortion was observed in 93% and 0% of the cases in full- and reduced-FOV images. Data conclusion Minimal distortion was achieved with RPG applied to reduced-FOV EPI data. RPG improved distortions for full-FOV images but with more modest improvements and limited correction near the nipple. Evidence level 3 TECHNICAL EFFICACY: Stage 1.

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e. total surface area, mean cortical thickness) using a genetically-informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, among which 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recounted loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant overlap of global and regional genetic modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

Physical exercise and cognitive training have the potential to enhance cognitive function and mobility in older adults at risk of Alzheimer's disease and related dementia (ADRD), but little is known about the feasibility of delivering multidomain interventions in home settings of older adults at risk of ADRD. This study aims to assess the feasibility of home-based delivery of exercise and cognitive interventions, and to evaluate the relationship between participants' intervention preferences and their subsequent adherence. Secondary objectives include the effect of the interventions on ADRD risk factors, including frailty, mobility, sleep, diet and psychological health.The SYNchronising Exercises, Remedies in GaIt and Cognition at Home (SYNERGIC@Home) feasibility trial is a randomised control trial that follows a 2×2 factorial design, with a 16-week home-based intervention programme (3 sessions per week) of physical exercises and cognitive training. Participants will be randomised in blocks of four to one of the following four arms: (1) combined exercise (aerobic and resistance)+cognitive training (NEUROPEAK); (2) combined exercise+control cognitive training (web searching); (3) control exercise (balance and toning)+cognitive training; and (4) control exercise+control cognitive training. SYNERGIC@Home will be implemented through video conferencing. Baseline and post-intervention assessments at 4-month and 10-month follow-up will include measures of cognition, frailty, mobility, sleep, diet and psychological health. Primary feasibility outcome is adherence to the interventions. Primary analytic outcome is the relationship between pre-allocation preference for a given intervention and subsequent adherence to the allocated intervention. A series of secondary analytic outcomes examining the potential effect of the individual and combined interventions on cognitive, mobility and general well-being will be measured at baseline and follow-up.Ethics approval was granted by the relevant research ethics boards. Findings of the study will be presented to stakeholders and published in peer-reviewed journals and at provincial, national and international conferences.NCT04997681, Pre-results.

The impact of statins, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) on coronavirus disease 2019 (COVID-19) severity and recovery is important given their high prevalence of use among individuals at risk for severe COVID-19. We studied the association between use of statin/angiotensin-converting enzyme inhibitors/ARB in the month before hospital admission, with risk of severe outcome, and with time to severe outcome or disease recovery, among patients hospitalized for COVID-19. We performed a retrospective single-center study of all patients hospitalized at University of California San Diego Health between February 10, 2020 and June 17, 2020 (n = 170 hospitalized for COVID-19, n = 5,281 COVID-negative controls). Logistic regression and competing risks analyses were used to investigate progression to severe disease (death or intensive care unit admission), and time to discharge without severe disease. Severe disease occurred in 53% of COVID-positive inpatients. Median time from hospitalization to severe disease was 2 days; median time to recovery was 7 days. Statin use prior to admission was associated with reduced risk of severe COVID-19 (adjusted OR 0.29, 95%CI 0.11 to 0.71, p < 0.01) and faster time to recovery among those without severe disease (adjusted HR for recovery 2.69, 95%CI 1.36 to 5.33, p < 0.01). The association between statin use and severe disease was smaller in the COVID-negative cohort (p for interaction = 0.07). There was potential evidence of faster time to recovery with ARB use (adjusted HR 1.92, 95%CI 0.81 to 4.56). In conclusion, statin use during the 30 days prior to admission for COVID-19 was associated with a lower risk of developing severe COVID-19, and a faster time to recovery among patients without severe disease.

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca’s area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

Glioblastoma is characterized by diffuse infiltration into the surrounding tissue along white matter tracts. Identifying the invisible tumour invasion beyond focal lesion promises more effective treatment, which remains a significant challenge. It is increasingly accepted that glioblastoma could widely affect brain structure and function, and further lead to reorganization of neural connectivity. Quantifying neural connectivity in glioblastoma may provide a valuable tool for identifying tumour invasion. Here we propose an approach to systematically identify tumour invasion by quantifying the structural connectome in glioblastoma patients. We first recruit two independent prospective glioblastoma cohorts: the discovery cohort with 117 patients and validation cohort with 42 patients. Next, we use diffusion MRI of healthy subjects to construct tractography templates indicating white matter connection pathways between brain regions. Next, we construct fractional anisotropy skeletons from diffusion MRI using an improved voxel projection approach based on the tract-based spatial statistics, where the strengths of white matter connection and brain regions are estimated. To quantify the disrupted connectome, we calculate the deviation of the connectome strengths of patients from that of the age-matched healthy controls. We then categorize the disruption into regional disruptions on the basis of the relative location of connectome to focal lesions. We also characterize the topological properties of the patient connectome based on the graph theory. Finally, we investigate the clinical, cognitive and prognostic significance of connectome metrics using Pearson correlation test, mediation test and survival models. Our results show that the connectome disruptions in glioblastoma patients are widespread in the normal-appearing brain beyond focal lesions, associated with lower preoperative performance (P < 0.001), impaired cognitive function (P < 0.001) and worse survival (overall survival: hazard ratio = 1.46, P = 0.049; progression-free survival: hazard ratio = 1.49, P = 0.019). Additionally, these distant disruptions mediate the effect on topological alterations of the connectome (mediation effect: clustering coefficient −0.017, P < 0.001, characteristic path length 0.17, P = 0.008). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of connectivity reorganization, where the increased neural connectivity is associated with better overall survival (log-rank P = 0.005). In conclusion, our connectome approach could reveal and quantify the glioblastoma invasion distant from the focal lesion and invisible on the conventional MRI. The structural disruptions in the normal-appearing brain were associated with the topological alteration of the brain and could indicate treatment target. Our approach promises to aid more accurate patient stratification and more precise treatment planning.

Carbapenem-resistant organisms (CROs) are characterized by high drug resistance, rapid transmission, and high lethality. Therefore, rapid detection for CROs is essential for appropriate applying antibiotics and implementing quarantine. Droplet digital chromogenic assays (DDCA) have been accepted as an effective means for rapid microbial detection as the small droplet volumes facilitate a significant enhancement in the local concentration of chromogenic factors and, therefore, reduce the required time of the test. Nevertheless, as their dependence on the time-consuming isolation culture, the DDCA is still associated with a long turnaround time. To overcome this limitation, we develop here a microfluidic chip-based CRO phenotypic identification method that integrates cascade filtration (CF) with DDCA (CF-DDCA). After a body fluid sample is introduced to the microfluidic chip, particles with sizes5 μm are removed out by the primary filter, and Gram (+) cocci with sizes1 μm removed out by the secondary filter so that only Gram (-) bacilli with sizes between 1.5 and 5 μm are selectively retained. The purified Gram (-) bacilli, along with chromogenic reagents and carbapenem antibiotics, are then subjected to the DDCA. We demonstrate that the CF can remove 99.9% of the interfering microorganisms and thus eliminates the isolating culture. Benefited from the isolating culture-free DDCA, phenotypic identification of CROs can be achieved within 3.5 h. Clinical urine sample testing shows that the sensitivity and specificity of the CF-DDCA for CRO identification are all 100%, and the total coincidence rate between CF-DDCA and the conventional assay is also 100%.

To determine characteristics most strongly associated with risk for aspiration events among head and neck cancer (HNC) patients undergoing curative intent treatment.This was a retrospective, cross-sectional study of 106 patients with previously untreated HNC who received definitive or postoperative radiation therapy (RT) +/- systemic therapy with curative intent. Patients who received post-treatment videofluoroscopic swallow study (VFSS) between 2018-2021 were included. Using ordinal multivariable logistic regression, we modeled the effects of age (60 years vs. ≤60 years), sex, body mass index (BMI) (20 kg/mOn ordinal multivariable logistic regression, age60 years (odds ratio (OR): 3.91, 95% confidence interval (CI): 1.29, 11.9), advanced stage (stage III-IVB) (OR: 3.13, 95% CI: 1.23, 7.79), pharyngeal constriction ratio (PCR)0.25 (OR: 3.65, 95% CI: 1.14, 11.7), and bolus clearance ratio (BCR)0.10 (OR: 3.42, 95% CI: 1.20, 9.75) were found to be significant risk factors for higher PAS scores. Patients with ≥ 2 pre-treatment risk factors had statistically significant increased risk for post-treatment aspiration (OR 2.52, 95% CI: 1.31, 4.86) on ordinal logistic regression. This model could be useful to direct high-risk patients toward interventions designed to reduce risk of aspiration events.

Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. Methods Participants were 278, 8–11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). Results Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. Conclusions There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

To test whether nomograms developed by NRG Oncology for oropharyngeal squamous cell carcinoma (OPSCC) patients could be validated in an independent population-based sample.The authors tested nomograms for estimating progression-free survival (PFS) and overall survival (OS) in patients from the Veterans Health Administration with previously untreated locoregionally advanced OPSCC, diagnosed between 2008 and 2017, managed with definitive radiotherapy with or without adjuvant systemic therapy. Covariates were age, performance status, p16 status, T/N category, smoking history, education history, weight loss, marital status, and anemia. We used multiple imputation to handle missing data and performed sensitivity analyses on complete cases. Validation was assessed via Cox proportional hazards models, log-rank tests, and c-indexes.A total of 4007 patients met inclusion criteria (658 patients had complete data). Median follow-up time was 3.20 years, with 967 progression events and 471 noncancer deaths. Each risk score was associated with poorer outcomes per unit increase (PFS score, hazard ratio [HR], 1.42 [1.37-1.47]; OS score, HR, 1.40 [1.34-1.45]). By risk score quartile, 2-year PFS estimates were 89.2%, 78.5%, 65.8%, and 48.3%; OS estimates were 92.6%, 83.6%, 73.9%, and 51.3%, respectively (P.01 for all comparisons). C-indices for models of PFS and OS were 0.65 and 0.67, for all patients, respectively (0.69 and 0.73 for complete cases). The nomograms slightly overestimated PFS and OS in the overall cohort but exhibited high agreement in complete cases.NRG nomograms were effective for predicting PFS and OS for patients with OPSCC, supporting their broader applicability in the OPSCC population undergoing definitive radiotherapy.

In this paper, we used experimental ecology methods and third-generation transcriptome sequencing to see the differences in growth, development, and reproduction of three strains of Daphniopsis tibetana Sars, 1903 from different locations in Tibet (Lake Namukacuo, NMKC; Lake Pengcuo, PC; and Lake Zigetangcuo, ZGTC). We also wanted to determine if the genes had changed after seawater-domesticated D. tibetana was reared in a laboratory. The results showed that at 15–16 ppt salinity and 15 ± 0.5 °C, the NMKC strain exhibited the highest survival rate of 26 d, and the ZGTC strain had the lowest survival rate at 53 days of culture. The body length was observed in NMKC (153.6 ± 12.1%), followed by PC (136.4 ± 16.1%), and then ZGTC (86.2 ± 7.6%). Combined, wild-type and seawater-acclimated D. tibetana produced 7252 DEGs, of which 4146 were up-regulated and 3106 were down-regulated. DEGs were subjected to gene ontology enrichment analysis. The DEGs were mainly enriched in single-organism localization, transporter activity, macromolecule localization, and organic substance transport. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis was also performed and revealed that the RNA transport, protein digestion and absorption, and protein processing in the endoplasmic reticulum pathways were highly enriched. The data mined can provide a reference for follow-up research.

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

Wastewater based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance as they are characterized by largely asymptomatic populations and are potential hotspots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (Geographic information systems) enabled building-level wastewater monitoring system associated with the on-campus residences of 7614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed within the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10 to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the “Return to Learn” program at UC San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. Covid-19 testing rates increased by 1.9-13X following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen.IMPORTANCEWastewater based epidemiology can be particularly valuable at University campuses where high-resolution spatial sampling in a well-controlled context could not only provide insight into what affects campus community as well as how those inferences can be extended to a broader city/county context. In the present study, a large-scale wastewater surveillance was successfully implemented on a large university campus enabling early detection of 85% of COVID-19 cases thereby averting potential outbreaks. The highly automated sample processing to reporting system enabled dramatically reduced the turnaround time to 5h (sample to result time) for 96 samples. Furthermore, miniaturization of the sample processing pipeline brought down the processing cost significantly ($13/sample). Taken together, these results show that such a system could greatly ameliorate long-term surveillance on such communities as they look to reopen.

Purpose: Cancer treatments can paradoxically appear to reduce the risk of noncancer mortality in observational studies, due to residual confounding. Here we introduce a method, Bias Reduction through Analysis of Competing Events (BRACE), to reduce bias in the presence of residual confounding. Experimental Design: BRACE is a novel method for adjusting for bias from residual confounding in proportional hazards models. Using standard simulation methods, we compared BRACE with Cox proportional hazards regression in the presence of an unmeasured confounder. We examined estimator distributions, bias, mean squared error (MSE), and coverage probability. We then estimated treatment effects of high versus low intensity treatments in 36,630 prostate cancer, 4,069 lung cancer, and 7,117 head/neck cancer patients, using the Veterans Affairs database. We analyzed treatment effects on cancer-specific mortality (CSM), noncancer mortality (NCM), and overall survival (OS), using conventional multivariable Cox and propensity score (adjusted using inverse probability weighting) models, versus BRACE-adjusted estimates. Results: In simulations with residual confounding, BRACE uniformly reduced both bias and MSE. In the absence of bias, BRACE introduced bias toward the null, albeit with lower MSE. BRACE markedly improved coverage probability, but with a tendency toward overcorrection for effective but nontoxic treatments. For each clinical cohort, more intensive treatments were associated with significantly reduced hazards for CSM, NCM, and OS. BRACE attenuated OS estimates, yielding results more consistent with findings from randomized trials and meta-analyses. Conclusions: BRACE reduces bias and MSE when residual confounding is present and represents a novel approach to improve treatment effect estimation in nonrandomized studies.