Presentation and Management of Granulomatous Mastitis in the United States: Results of an American Society of Breast Surgeons Registry Study.

BACKGROUND: Granulomatous mastitis (GM) is a benign, chronic, inflammatory disease lacking clear treatment guidelines. The purpose of this American Society of Breast Surgeons (ASBrS) prospective, multisite registry was to characterize the presentation of GM and identify treatment strategies associated with symptom resolution and optimal cosmesis. METHODS: ASBrS members entered data into a registry on patient demographics, treatment, symptoms, and cosmesis over a 1-year period. Initial symptoms were graded as mild, moderate, or severe. The Chi-square test and logistic regression were used to identify factors related to symptom improvement and cosmesis. RESULTS: Overall, 112 patients with a mean age of 36 years were included. More patients were Hispanic (49.1%) and from the Southwest (41.1%), and management included observation (4.5%), medical (70.5%), surgical (5.4%), or combination treatment (19.6%). Immunosuppression was used in 83 patients (74.1%), including 43 patients who received intralesional steroid injections. Patients with severe symptoms were more likely to undergo surgical intervention compared with those with mild or moderate symptoms (21.4% vs. 0% and 7.5%, respectively; p = 0.004). Within 1 year, 85 patients (75.9%) experienced symptom improvement and/or resolution at a median of 3 months. Receipt of immunosuppressive therapy was predictive of improvement or resolution at 1 month (odds ratio 4.22; p = 0.045). One-year physician-assessed cosmesis was excellent or good for 20/35 patients (57.1%) and was not associated with type of treatment or symptom severity. CONCLUSION: Although GM can have a protracted course, the majority of patients in this registry resolved within 1 year, with good cosmetic result. Treatment with immunosuppression appears to be most beneficial, and a symptom-based algorithm may be helpful to guide treatment.

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

BACKGROUND: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. PURPOSE: The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction. METHODS: Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. RESULTS: Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = -0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder = -0.77%/1.00%/-0.66%/-1.53%, rectum = 1.11%/-0.22%/-0.29%/-3.37%, sigmoid = -0.47%/-0.06%/-2.37%/-1.40%, and ME D90 = 2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription. CONCLUSIONS: 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.

Let's TOC Fertility: A stepped wedge cluster randomized controlled trial of the Telehealth Oncofertility Care (TOC) intervention in children, adolescent and young adult cancer survivors.

INTRODUCTION: Children, adolescent, and young adult cancer survivors experience overall increased risks of infertility that are preventable through effective fertility preservation services prior to starting cancer treatment. Oncofertility care is the evidence-based practice of informing newly diagnosed cancer patients about their reproductive risks and supporting shared decision-making on fertility preservation services. Despite longstanding clinical guidelines, oncofertility care delivery continues to be limited and highly variable across adult and pediatric oncology settings. MATERIALS AND METHODS: We describe the design of a stepped wedge cluster randomized clinical trial to evaluate the effectiveness of the multi-component Telehealth Oncofertility Care (TOC) intervention conducted in 20 adult and pediatric oncology clinics across three health systems in Southern California. Intervention components are: 1) electronic health record-based oncofertility needs screen and referral pathway to a virtual oncofertility hub; 2) telehealth oncofertility counseling through the hub; and 3) telehealth oncofertility financial navigation through the hub. We hypothesize the intervention condition will be associated with increased proportions of patients who engage in goal-concordant oncofertility care (i.e., engagement in reproductive risk counseling and fertility preservation services that meet the patient's fertility goals) and improved patient-reported outcomes, compared to the usual care control condition. We will also evaluate intervention implementation in a mixed-methods study guided by implementation science frameworks. DISCUSSION: Our overall goal is to speed implementation of a scalable oncofertility care intervention at cancer diagnosis for children, adolescent and young adult cancer patients to improve their future fertility and quality of life. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT05443737.

Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized ß ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

National norms for the obstetric nurses' and midwives' health education competence, and its influencing factors: a nationwide cross-sectional study.

BACKGROUND: Strengthening obstetric nurses' and midwives' health education competence is the investment and guarantee for the population's future health. The purpose of study is to establish national norms for their health education competence, and explore possible influencing factors for providing an uniform criterion identifying levels and weaknesses. METHODS: An online questionnaire with a standard process was used to collect data. Three normative models were constructed, and multiple linear regression analysis analyzed possible influencing factors. RESULTS: The sample respondents (n = 3027) represented obstetric nurses and midwives nationally. Three health education competency normative norms (mean, percentile and demarcation norm) were constructed separately. Locations, hospital grade, department, marital status, training times and satisfaction with health education training influenced obstetrical nurses' and midwives' health education competence (P<0.05). CONCLUSION: This study constructed the first national standard for assessing obstetric nurses' and midwives' health education competence, providing a scientific reference to evaluate the degree of health education competence directly. These known factors could help clinical and policy managers designate practice improvement measures. In future research, Grade I hospitals should be studied with larger sample sizes, and indicators need to improve to reflect health education's effect better.

Effect of Phase Encoding Direction on Image Quality in Single-Shot EPI Diffusion-Weighted Imaging of the Breast.

BACKGROUND: In breast diffusion-weighted imaging (DWI), distortion and physiologic artifacts affect clinical interpretation. Image quality can be optimized by addressing the effect of phase encoding (PE) direction on these artifacts. PURPOSE: To compare distortion artifacts in breast DWI acquired with different PE directions and polarities, and to discuss their clinical implications. STUDY TYPE: Prospective. POPULATION: Eleven healthy volunteers (median age: 47 years old; range: 22-74 years old) and a breast phantom. FIELD STRENGTH/SEQUENCE: Single-shot echo planar DWI and three-dimensional fast gradient echo sequences at 3 T. ASSESSMENT: All DWI data were acquired with left-right, right-left, posterior-anterior, and anterior-posterior PE directions. In phantom data, displacement magnitude was evaluated by comparing the location of landmarks in anatomical and DWI images. Three breast radiologists (5, 17, and 23 years of experience) assessed the presence or absence of physiologic artifacts in volunteers' DWI datasets and indicated their PE-direction preference. STATISTICAL TESTS: Analysis of variance with post-hoc tests were used to assess differences in displacement magnitude across DWI datasets and observers. A binomial test and a chi-squared test were used to evaluate if each in vivo DWI dataset had an equal probability (25%) of being preferred by radiologists. Inter-reader agreement was evaluated using Gwet's AC1 agreement coefficient. A P-value <0.05 was considered statistically significant. RESULTS: In the phantom study, median displacement was the significantly largest in posterior-anterior data. While the displacement in the anterior-posterior and left-right data were equivalent (P = 0.545). In the in vivo data, there were no physiological artifacts observed in any dataset, regardless of PE direction. In the reader study, there was a significant preference for the posterior-anterior datasets which were selected 94% of the time. There was good agreement between readers (0.936). DATA CONCLUSION: This study showed the impact of PE direction on distortion artifacts in breast DWI. In healthy volunteers, the posterior-to-anterior PE direction was preferred by readers. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Sociodemographic correlates of parent and youth-reported eating disorder symptoms in the Adolescent Brain Cognitive Development Study.

Purpose: Eating Disorders (EDs) often start in adolescence, though ED-related concerns in diverse youth samples remain understudied. We leveraged data from the Adolescent Brain Cognitive Development (ABCD) Study to identify the prevalence of parent- and youth-reported ED symptoms and their sociodemographic characteristics. Methods: Data were drawn from baseline (ages 9-11 years, n=11,868) and 2-year follow-up (ages 11-14 years; n=10,908) from the ABCD Study. A tetrachoric factor analysis summarized clusters of ED symptoms, which were compared between parent and youth reports and across sociodemographic variables. Results: Three factors emerged reflecting "weight distress", "weight control", and "binge eating" (prevalence range: 1.5-7.3%). Symptoms loaded onto similar factors between reporters. Rates of symptom endorsement were similar between sexes, with disproportionately higher endorsement rates for youth who self-identified as sexual minority, Hispanic, Black, or Mixed race participants, and those from a disadvantaged socioeconomic background, compared to the reference ABCD sample. Youth and parent reports at 2-year showed ~12% overlap. Conclusions: ED-related concerns among historically understudied racial and sexual minority groups call for greater attention to the detection and treatment of these symptoms in these groups. Applying a transdiagnostic approach to ED symptoms can inform effective detection and intervention efforts.

The effect of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids.

The use of patient-derived organoids (PDOs) to characterize therapeutic sensitivity and resistance is a promising precision medicine approach, and its potential to inform clinical decisions is now being tested in several large multiinstitutional clinical trials. PDOs are cultivated in the 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 effect of different sources of BMEs on organoid drug response is unknown. Here, we tested the effect of BME source on proliferation, drug response, and gene expression in mouse and human pancreatic ductal adenocarcinoma (PDA) organoids. Both human and mouse organoids displayed increased proliferation in Matrigel compared with Cultrex and UltiMatrix. However, we observed no substantial effect on drug response when organoids 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 found that the BME source (Matrigel, Cultrex, UltiMatrix) does not shift PDO dose-response curves or drug testing results, indicating that PDO pharmacotyping is a robust approach for precision medicine.

Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models.

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.

Community-engaged optimization of COVID-19 rapid evaluation and testing experiences: roll-out implementation optimization trial.

BACKGROUND: There continues to be a need for COVID-19 testing that is pragmatic, community-centered, and sustainable. This study will refine and test implementation strategies prioritized by community partners: (1) walk-up no-cost testing, (2) community health worker (promotores)-facilitated testing and preventive care counseling, (3) vending machines that dispense no-cost, self-testing kits. METHODS: A co-designed Theory of Change from an earlier study phase and the Practical, Robust Implementation and Sustainment Model (PRISM) will guide the study design, measures selection, and evaluation. The first aim is to refine and operationalize a multi-component implementation strategy bundle and outcome measures for COVID-19 testing. A Community and Scientific Advisory Board (CSAB) will be established and include community members, clinical providers/staff from the partnering Federally Qualified Health Center (FQHC), public health researchers, policymakers, and a county health department ambassador. Engagement of CSAB members will be assessed through structured ethnography and a survey about the quality and quantity of engagement practices. The second aim is to implement and evaluate the impact of the implementation strategy bundle to optimize COVID-19 testing in communities using a roll-out implementation optimization (ROIO) design. Seven thousand and five hundred community members will be enrolled across four FQHC clinics over 18 months. Participants will be invited to complete an electronic survey about their demographics, health, and COVID-19 testing results and experiences. CSAB members and clinic partners will participate in PRISM fit and determinant assessments prior to each clinic rollout and post-trial. Interviews will be conducted with 60 community participants and 12 providers/staff following a 3-month rollout period at each clinic, inquiring about their experiences with the implementation strategies. Quantitative data will be analyzed using hierarchical multilevel models to determine the impact of implementation strategies. Qualitative data will be analyzed using rapid qualitative approaches to summarize implementation experiences and identify necessary changes prior to subsequent rollouts. A matrix approach will be used to triangulate data from quantitative and qualitative sources based on PRISM domains. DISCUSSION: This is one of the first pragmatic implementation trials to use a ROIO design and aims to co-create a sustainable and equitable COVID-19 testing program. Findings are likely to generalize to other public health prevention efforts. TRIAL REGISTRATION: NCT05894655 March 2, 2023.

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).

BACKGROUND/OBJECTIVES: Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS: Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS: After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS: Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods.

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.

Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness.

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, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered 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 genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

The impact of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids.

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.

Outcomes from a 3-fraction high-dose-rate brachytherapy regimen for patients with cervical cancer.

PURPOSE: To estimate local control, survival, and toxicity associated with a 3-fraction (3F) image-guided brachytherapy (IGBT) regimen compared to longer fraction (LF) for cervical cancer. METHODS: 150 patients treated between 2015-2020 with 3F (24Gy in 3 fractions) or LF (28...30 Gy in 4-5 fractions) were reviewed. The primary outcome was 2-year local failure. We compared overall survival (OS), disease-free survival (DFS), hospitalizations, and toxicity. RESULTS: There were 32 patients in the 3F group and 118 in the LF group, with a median follow up of 22 months. The 3F had worse performance status (p = 0.01) but otherwise similar characteristics. The 2-year local failure rate was 3.6% (95% CI 0%, 10.6%) for 3F, and 7.5% (95% CI 2.4%, 12.6%) for LF. The univariable hazard ratio (HR) for local failure for 3F was 0.43 (0.05, 3.43; p = 0.43). Moreover, 2 of 32 (6.3%) 3F patients experienced Grade ...3 toxicity compared to 7 of 118 (5.9%) LF patients (p = 1.0), with no difference in hospitalization within 2 years (p = 0.66) and no treatment-related deaths. CONCLUSIONS: Local control was excellent, with long term survival and toxicity similar between the groups. These findings support consideration of 3F.

Droplet Encoding-Pairing Enabled Multiplexed Digital Loop-Mediated Isothermal Amplification for Simultaneous Quantitative Detection of Multiple Pathogens.

Despite the advantages of digital nucleic acid analysis (DNAA) in terms of sensitivity, precision, and resolution, current DNAA methods commonly suffer a limitation in multiplexing capacity. To address this issue, a droplet encoding-pairing enabled DNAA multiplexing strategy is developed, wherein unique tricolor combinations are deployed to index individual primer droplets. The template droplets and primer droplets are sequentially introduced into a microfluidic chip with a calabash-shaped microwell array and are pairwise trapped and merged in the microwells. Pre-merging and post-amplification image analysis with a machine learning algorithm is used to identify, enumerate, and address the droplets. By incorporating the amplification signals with droplet encoding information, simultaneous quantitative detection of multiple targets is achieved. This strategy allows for the establishment of flexible multiplexed DNAA by simply adjusting the primer droplet library. Its flexibility is demonstrated by establishing two multiplexed (8-plex) droplet digital loop-mediated isothermal amplification (mddLAMP) assays for individually detecting lower respiratory tract infection and urinary tract infection causative pathogens. Clinical sample analysis shows that the microbial detection outcomes of the mddLAMP assays are consistent with those of the conventional assay. This DNAA multiplexing strategy can achieve flexible high-order multiplexing on demand, making it a desirable tool for high-content pathogen detection.

Quantitative prediction of aspiration risk in head and neck cancer patients treated with radiation therapy.

OBJECTIVE: To determine characteristics most strongly associated with risk for aspiration events among head and neck cancer (HNC) patients undergoing curative intent treatment. MATERIALS AND METHODS: 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/m2 vs. ≤20 kg/m2), American Joint Committee on Cancer 8th edition stage (I-II vs. III-IVB), treatment with cisplatin (vs. other or no systemic therapy), post-operative status, primary site (oral cavity vs. P16+ oropharynx vs. P16- Mucosal Site vs. other), and quantitative VFSS measures on Penetration-Aspiration Scale (PAS) score. RESULTS AND CONCLUSION: On ordinal multivariable logistic regression, age >60 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.

Cascade filtration and droplet digital detection integrated microfluidic assay enables isolating culture-free phenotypic identification of carbapenem-resistant organisms.

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 sizes >5 µm are removed out by the primary filter, and Gram (+) cocci with sizes <1 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%.

Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients.

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.

A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS.

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.