Notes

[Management associated with metastatic hormone-naive cancer of the prostate in diagnosis].
We carried out One-way ANOVA and Duncan's new multiple range method to analyze different dying approaches and conditions. The dyed fabrics were evaluated by indicators of color strength and fastness, including washing, rubbing, and perspiration. The findings revealed the potentiality of biomordants to improve the dyeing properties of madder. The dyeing properties of recycled madder were marginally better than traditional madder. The results revealed the feasibility of enhancing the dyeing property and reuse of the residue from madder dyeing. The improved dyeing and reuse of residue can improve local ecological, economic benefits, and cultural heritage while applying research findings for the subsequent commercialization of plant dyes.Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (JSN) from low-resolution images (i.e., X-ray images) are mostly subjective. We implement an objective assessment and quantification of KOA to aid practitioners. In particular, we developed an interpretable ensemble of convolutional neural network (CNN) models consisting of three modules. First, we developed a scale-invariant and aspect ratio preserving model to localize Knee joints. Second, we created multiple instances of "hyperparameter optimized" CNN models with diversity and build an ensemble scoring system to assess the severity of KOA according to the Kellgren-Lawrence grading (KL) scale. Third, we provided visual explanations of the predictions by the ensemble model. We tested our models using a collection of 37,996 Knee joints from the Osteoarthritis Initiative (OAI) dataset. Our results show a superior (13-27%) performance improvement compared to the state-of-the-art methods.Medical oxygen concentrators (MOCs) are used for supplying medical grade oxygen to prevent hypoxemia-related complications related to COVID-19, chronic obstructive pulmonary disease (COPD), chronic bronchitis and pneumonia. MOCs often use a technology called pressure swing adsorption (PSA), which relies on nitrogen-selective adsorbents for producing oxygen from ambient air. MOCs are often designed for fixed product specifications, thereby limiting their use in meeting varying product specifications caused by a change in patient's medical condition or activity. To address this limitation, we design and optimize flexible single-bed MOC systems that are capable of meeting varying product specification requirements. Specifically, we employ a simulation-based optimization framework for optimizing flexible PSA- and pressure vacuum swing adsorption (PVSA)-based MOC systems. Detailed optimization studies are performed to benchmark the performance limits of LiX, LiLSX and 5A zeolite adsorbents. Relacorilant The results indicate that LiLSX outperforms both LiX and 5A, and can produce 90% pure oxygen at 21.7 L/min. Moreover, the LiLSX-based flexible PVSA system can manufacture varying levels of oxygen purity and flow rate in the range 93-95.7% and 1-15 L/min, respectively. The flexible MOC technology paves way for transitioning to an envisioned cyber-physical system with real-time oxygen demand sensing and delivery for improved patient care.This study presented the fabrication of macro and micro-scale microbial fuel cells (MFCs) to generate bioelectricity from oxalate solution and monitor the biodegradation in a micro-scale MFC for the first time. The maximum generated power density of 44.16 W m-3 in the micro-scale MFC elucidated its application as a micro-sized power generator for implantable medical devices (IMDs). It is also worthwhile noting that for the macro-scale MFC, the significant amounts of open circuit voltage, oxalate removal, and coulombic efficiency were about 935 mV, 99%, and 44.2%, respectively. These values compared to previously published studies indicate successful oxalate biodegradation in the macro-scale MFC. Regarding critical challenges to determine the substrate concentration in microfluidic outlets, sample collection in a suitable time and online data reporting, an analogy was made between macro and micro-scale MFCs to elicit correlations defining the output current density as the inlet and the outlet oxalate concentration. Another use of the system as an IMD is to be a platform to identify urolithiasis and hyperoxaluria diseases. As a versatile device for power generation and oxalate biodegradation monitoring, the use of facile and cheap materials ( less then  $1.5 per device) and utilization of human excreta are exceptional features of the manufactured micro-scale MFC.Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.
Modeling of dose distribution of randomly moving population around a radioactive source is a complex problem.

The objective is to develop a model and solution techniques to estimate radiation absorbed dose to the population randomly moving around a radioactive source.

The problem is formulated using a second-order partial differential equation; different moments of the dose distribution function are defined related to physically realizable quantities, and solutions are obtained using standard moments methods. Alternatively, numerical simulations are performed to estimate the radiation doses using Monte Carlo approach for individual positions and random motions of the people around the source.

A good agreement is found between average doses obtained from moments method and numerical simulations. A typical application of this model to different exposure conditions shows that the average dose is highly dependent on the population density. The study results show that average dose decreases with increase in the population density and movement area of random walker.

This mathematical model can be used as a rapid assessment tool by the emergency planners in resource optimization by providing quick estimates of likely exposures for triage and emergency response.
This mathematical model can be used as a rapid assessment tool by the emergency planners in resource optimization by providing quick estimates of likely exposures for triage and emergency response.
The new subgroup screening tool "subscreen" aims to understand the unclear and complex association between socioeconomic status (SES) and childhood allergy. This software R package has been successfully used in clinical trials but not in large population-based studies.

To screen and identify subgrouping factors explaining their impact on the association between SES and respiratory allergies in childhood and youth.

Using the national German childhood and youth survey dataset (KiGGS Wave 2), we included 56 suspected subgrouping factors to investigate the association between SES (low vs. high) and allergic rhinitis and/or asthma in an exploratory manner. The package enabled a comprehensive overview of odds ratios when considering the SES impact per subgroup and analogously all disease proportions per subgroup.

Among the 56 candidate factors, striking subgrouping factors were identified; e.g., if mothers were younger and in the low SES group, their children had a higher risk of asthma. In addition children of the teen's age were associated with increased risks in the low SES group. For the crude proportions, factors such as (parental) smoking or having had no "contact with farm animals" were identified as strong risk factors for rhinitis.

The "subscreen" package enabled the detection of notable subgroups for further investigations exemplarily for similar epidemiological research questions.
The "subscreen" package enabled the detection of notable subgroups for further investigations exemplarily for similar epidemiological research questions.The positive surgical margin (PSM) and biochemical recurrence (BCR) are two main factors associated with poor oncotherapeutic outcomes after prostatectomy. This is an Asian population study based on a single-surgeon experience to deeply investigate the predictors for PSM and BCR. We retrospectively included 419 robot-assisted radical prostatectomy cases. The number of PSM cases was 126 (30.1%), stratified as 22 (12.2%) in stage T2 and 103 (43.6%) in stage T3. Preoperative prostate-specific antigen (PSA) > 10 ng/mL (p = 0.047; odds ratio [OR] 1.712), intraoperative blood loss > 200 mL (p = 0.006; OR 4.01), and postoperative pT3 stage (p  3 (p = 0.002; HR 2.689) was a significant BCR predictor. These results indicate that PSA and pathological status are key factors influencing PSM and BCR.Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction-separation (T-S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T-S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T-S relations and identify the imperfection locations at interface, respectively.
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