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This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis.

This study examines how quadratic polynomials, which are mathematical equations used to model and understand patterns in data, can be effectively applied using Python, a versatile programming language with libraries suited for mathematical and visual analysis. Researchers have focused on the adaptability of these polynomials in various fields, from software analytics to materials science, in order to provide practical Python code examples. They also discussed the predictive accuracy of the method, confirmed through a statistical measure called R-squared, and acknowledged the need for future research to integrate more complex models for richer data interpretation.

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In a typical undergraduate biology curriculum, students do not dive into research until they first wade through large amounts of content. Biology courses in the first few years of the college curriculum tend to be lecture-based and exam-based courses. As a result, science students are mainly exposed to content knowledge-not the skills scientists practice daily. While students may practice manual techniques in lab sections of lecture courses, the higher-level analytical research skills are reserved for the final semesters of college. To address this issue, we created an undergraduate cell biology course centered around practicing research skills, and fully accessible to students with no prerequisite content knowledge. In our course, students read primary literature (no textbooks) and were assessed by writing 12 analytical response papers and a full research proposal (no exams). Each student chose a topic for their semester-long project, conducted a literature review, and proposed future experiments-all in a stepwise fashion with plentiful feedback. The students' thorough comprehension of the primary literature, along with successful completion of the research proposals, shows that the course achieved its goals of building these skills-even in the nonbiology majors taking this pilot course. Pre- and post-survey results demonstrate that students gained feelings of confidence and preparedness for future research experiences. We envision a future model in which such a skills-based course replaces a more traditional cell biology course, giving students the opportunity to practice high-level analytical research skills from very early on in the undergraduate biology curriculum.

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PURPOSE: To evaluate the agreement between 24-2 visual field (VF) test results obtained using the gaze analyzing perimeter (GAP; Findex) and the Humphrey field analyzer (HFA; Carl Zeiss Meditec). DESIGN: Cross-sectional study. PARTICIPANTS: Patients underwent HFA 24-2 for suspected or confirmed VF loss and were treated at the Kyoto University Hospital between December 2022 and July 2023. METHODS: Patients underwent consecutive VF tests on the same eye using HFA and GAP 24-2 tests. Bland-Altman analysis was used to compare GAP and HFA results. Examination points where the sensitivity measured using GAP was ≥ 10 dB higher than that measured using HFA were re-evaluated by referring back to the original gaze data; 2 ophthalmologists assessed whether the gaze moved linearly toward the new test target. MAIN OUTCOME MEASURES: Mean deviation (MD) and elapsed time on an individual basis and sensitivity on an examination point basis. RESULTS: Forty-seven eyes of 47 patients were analyzed. The correlation coefficient of the MD using HFA and GAP was 0.811 (95% confidence interval [CI]: 0.683-0.891). Bland-Altman analysis showed good agreement between HFA and GAP tests. The mean difference (95% limits of agreement) in MD between HFA and GAP results was -0.63 dB (-5.81 to 4.54 dB). Although no statistically significant differences were observed in the elapsed time (P = 0.99), measurements completed within 200 seconds were observed only in the GAP group (11 cases, 23.4%), who had significantly better HFA MD value than others (P = 0.001). On an examination point basis for sensitivity, the correlation coefficient between HFA and GAP was 0.691 (95% limits of agreement, 0.670-0.711). Original gaze data assessment revealed that the gaze moved linearly toward the new test target for 70.2% of the examination points with a sensitivity discrepancy. CONCLUSIONS: The results indicate that the GAP provides VF assessment outcomes comparable to those of the HFA. The GAP exhibited advantages in terms of testing time, particularly in patients with minimal VF impairment. Furthermore, the GAP records all eye movements, enabling the objective determination of VF abnormalities based on gaze patterns and facilitating easy posthoc verification. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications.

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Introdution: During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity, investigators must often manually measure cell orientations, which is a time-consuming endeavor. To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). Methods: PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. For validation, inner ear sensory epithelia including cochleae and utricles from mice were immunostained for ßII-spectrin and imaged with a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear hair cell angle measurements for accuracy using a one to five agreement scale. For utricle samples, PCPA derived measurements were directly compared against manually derived angle measurements and the concordance correlation coefficient (CCC) and Bland-Altman limits of agreement were calculated. PCPA was also tested against previously published images examining PCP in various tissues and across various species suggesting fairly broad utility. Results: PCPA was able to recognize and count 99.81% of cochlear hair cells, and was able to obtain ideally accurate planar cell polarity measurements for at least 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to 98%-100% for all users and across all samples. When PCPA's measurements were compared with manual angle measurements for E17.5 utricles there was negligible bias (<0.5°), and a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Discussion: Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

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Traditional methods for assessing the cleanliness of liquid metal are characterized by prolonged detection times, delays, and susceptibility to variations in sampling conditions. To address these limitations, an online cleanliness-analyzing system grounded in the method of the electrical sensing zone has been developed. This system facilitates real-time, in situ, and quantitative analysis of inclusion size and amount in liquid metal. Comprising pneumatic, embedded, and host computer modules, the system supports the continuous, online evaluation of metal cleanliness across various metallurgical processes in high-temperature environments. Tests conducted with gallium liquid at 90 °C and aluminum melt at 800 °C have validated the system's ability to precisely and quantitatively detect inclusions in molten metal in real time. The detection procedure is stable and reliable, offering immediate data feedback that effectively captures fluctuations in inclusion amount, thereby meeting the metallurgical industry's demand for real-time analyzing and control of inclusion cleanliness in liquid metal. Additionally, the system was used to analyze inclusion size distribution during the hot-dip galvanizing process. At a zinc melt temperature of 500 °C, it achieved a detection limit of 21 µm, simultaneously providing real-time data on the size and amount distribution of inclusions. This represents a novel strategy for the online monitoring and quality control of zinc slag throughout the hot-dip galvanizing process.

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This method introduces a methodological approach for comprehensively analyzing COVID-19 outbreaks using Gaussian pulse models to assess transmission rates. Our methodology is designed to provide an in-depth understanding of the intricate dynamics underlying the spread of COVID-19. By incorporating Gaussian pulse models into our approach, we capture temporal and spatial outbreaks' characteristics with high precision. This method provides a detailed overview of our methodological approach, underscoring its potential to revolutionize our comprehension of COVID-19 outbreak dynamics.•Our methodology involves the application of Gaussian pulse models to the transmission rate estimation.•Parameter estimation in epidemic modeling involves adjusting key factors like transmission rate, and effective reproduction number to best match observed data.•We use Microsoft Excel's Solver add-in with the GRG algorithm to find the best parameter values, improving the data fit.

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Developing a knob-into-hole asymmetric bispecific IgG1 monoclonal antibody (mAb) poses manufacturing challenges due to the expression of chain pairing variants, also called mispaired species, in the desired product. The incorrect pairing of light and heavy chains could result in heterogeneous mispaired species of homodimers, heterodimers, light chain swapping, and low molecular weight species (LMWS). Standard chromatography, capillary electrophoretic, or spectroscopic methods poorly resolve these from the main variants. Here, we report a highly sensitive reverse-phase polyphenyl ultra-high-performance liquid chromatography (RP-UHPLC) method to accurately measure mispaired species of Duet mAb format, an asymmetric IgG1 bispecific mAb, for both process development and quality control analytical tests. Coupled with electrospray ionization mass spectrometry (ESI-MS), it enabled direct online characterization of mispaired species. This single direct assay detected diverse mispaired IgG-like species and LMWS. The method resolved eight disulfide bonds dissociated LMWS and three mispaired LMWS. It also resolved three different types of IgG-like mispaired species, including two homodimers and one heterodimer. The characterization and quantification simultaneously enabled the cell line selection that produces a lesser heterogeneity and lower levels of mispaired species with the desired correctly paired product. The biological activity assessment of samples with increased levels of these species quantified by the method exhibited a linear decline in potency with increasing levels of mispaired species in the desired product. We also demonstrated the utility of the technique for testing in-process intermediate materials to determine and assess downstream purification process capability in removing diverse mispaired IgG-like species and LMWS to a certain level during the downstream purification process. Our investigation demonstrates that adopting this method was vital in developing asymmetric bispecific mAb from the initial stage of cell line development to manufacturing process development. Therefore, this tool could be used in the control strategy to monitor and control mispaired species during manufacturing, thus improving the quality control of the final product.

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The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices' location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.

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BACKGROUND: Motivation is the process whereby goal-directed activities are initiated and sustained. Motivation is a crucial factor in academic achievement. The study aims to measure students' demographic factors and external environments' effect on their motivation and determine the impact of students' motivation and self-efficacy on their learning engagement and academic performance. METHODOLOGY: This is a cross-sectional study that involved distributing an online digital questionnaire, which was applied in the capital of Saudi Arabia, "Riyadh." The students' motivation was assessed using three scales that are designed to measure the students' intrinsic/extrinsic motivation, self-efficacy, and learning engagement. RESULTS: In this study, we collected 429 responses from our distributed questionnaire among medical students where males represented 60.1% of the sample. Moreover, we classified the satisfaction level into five subcategories: very satisfied, satisfied, neutral, unsatisfied, and very unsatisfied. We found that most of the students (38.7%) were satisfied with their academic performance, while 17.7% were strongly satisfied. The mean enrollment motivation score in this study was 19.83 (SD 2.69), and when determining its subcategories, we found that the mean intrinsic motivation score was 10.33 (out of 12) and the mean extrinsic motivation score was 10.23 (out of 12). Moreover, the mean self-efficacy score was 9.61 and the mean learning engagement score was 8.97 (out of 12). Moreover, we found that a longer duration needed by the students to reach the college from their residence is significantly associated with lower learning engagement reported by the students (8.54 vs. 9.13 in shorter times, P=0.034). Finally, we found that students who entered medical school as their first choice had significantly higher intrinsic motivation, extrinsic motivation, self-efficacy, and learning engagement. CONCLUSION: A student's preference for entering medical school will affect their motivation, self-efficacy, and learning engagement. Moreover, intrinsic and extrinsic motivations significantly correlate with self-efficacy and satisfaction with academic performance; however, they have no effect on the grade point average (GPA) of the last semester. The only factor that positively correlates with students' GPA is learning engagement.

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With the rapid spread of epidemic situation, how to quickly analyze health risk factors has become a major challenge in the current public health field. The development of natural language processing (NLP) technology allows us to quickly capture the details of dealing with unknown health risks from massive data. This study uses official case reports on the Chinese Internet since the outbreak of Delta to analyze, and tries to use NLP technology to quickly analyze relevant health risk factors. This research attempts to use NLP-related technologies to interpret the main health risks before the goal, and to provide research methods and ideas for future response to unknown health challenges. This study found that it is difficult for non-public health professionals to understand the health risk factors interpreted by NLP. At present, the use of NLP to analyze cases related to health threats is not practical, and its role is mainly reflected in improving the efficiency of large-scale data analysis and decision support.

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Objective: To clarify the concept of spiritual needs and explain its meaning to older adults with cancer. Methods: Electronic databases (Web of Science, PubMed, EBSCOASU, CNKI, Wanfang, and VIP) were systematically searched and analyzed using "spiritual needs" as keywords. Rodgers' evolutionary method guided the concept analysis to identify attributes, antecedents, and consequences. Two rounds of Delphi expert consultations ensured accuracy, reliability, and feasibility for implementation. Results: Spiritual needs express an individual's expectations of comfort and inner peace that satisfy his or her perception of the meaning and purpose of life, the ability to love and be loved, feelings of peace and gratitude, and a sense of belonging and hope. Spiritual needs have four dimensions: personal, communal, environmental, and transcendence or supreme. The attributes of spiritual needs include meaning and purpose of life, love and being loved, peace and gratitude, belonging, and hope. The antecedents include spiritual recognition and events that trigger spiritual needs and spiritual need thresholds. The outcomes of addressing and meeting the spiritual needs of older adults with cancer include promoting their spiritual health and enhancing their quality of life. After two rounds of Delphi experts' consultation, the expert authority coefficients (Cr) were 0.83 and 0.88, respectively. Experts agreed on the concept of spiritual needs. Conclusions: Exploring antecedents of spiritual needs in older adults with cancer clarifies obstacles to spiritual practice, offering intervention strategies for spiritual care and well-being. Meeting their spiritual needs enhances spiritual health and quality of life, essential in humanistic nursing care.

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AIM: The aim of our investigations is to optimize the anatomical basis for the design of a sufficient occlusal relationship, especially in view of the innovative technologies by analyzing the occlusal contact point patterns at cusp structures according to A-, B-, C- localization tooth by tooth on the individual occlusal surfaces in the posterior region in static habitual occlusal position. MATERIALS AND METHODS: In 3300 subjects of the population-based Study of Health in Pomerania (SHIP 1) the interocclusal registration in habitual intercuspation using silicone registration was used and analyzed by using the special evaluation software Greifswald Digital Analyzing System (GEDAS II). Chi square test was used to investigate whether the distribution of contact areas differed in the group of premolars or molars - each considered separately for maxilla and mandible - on the basis of the probability of error p < 0.05. RESULTS: In 709 subjects (446 male with a mean age of 48.9 ± 13.04 years; 283 female with a mean age of 52.4 ± 14.23 years) the antagonistic situation was specifically considered on natural posterior teeth without conservative or restorative-prosthetic interventions, i.e. without caries, fillings, crowns or other restorations. On the basis of these subjects, the silicone registrations were analyzed using GEDAS II. For the first and second upper molars, the ABC contact distribution was the most frequent: 20.4 % for the first and 15.3 % for the second molar. The second most frequent contact area for maxillary molars was area 0. The upper molars had contact areas only at the maxillary palatal cusp (B-/C-contacts). This contact relationship was most frequent in the maxillary premolar (18.1-18.6 %). In mandibular premolars, with the buccal cusps areas A and B were frequently involved (15.4-16.7 %). Mandibular molars showed a frequent contact pattern involving all A-, B-, C- and 0- contact areas (13.3-24.2 %). To capture the possible influence of the antagonistic dentition situation, the antagonistic situation was specifically considered and except for the mandibular premolars (p < 0.05) the contact distribution did not differ for molars and maxillary premolars regarding the dental status of the antagonistic teeth. Natural posterior teeth without occlusal contacts were observed from 20.0 % in the second lower molars to 9.7 % in the first upper molars. CONCLUSION: Our results suggest a clinically relevant due to the fact, that this study is the first population-based epidemiological study to analyze the occlusal contact point patterns at cusp structures according to A-, B-, C- localization tooth by tooth on the individual occlusal surfaces in the posterior region in static habitual occlusal position in order to optimize the anatomical basis for the design of a sufficient occlusal relationship.

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This method describes the use of thick round borosilicate glass micro-channels for blood flow visualization using micro-particle image velocimetry (µPIV) techniques. In contrast with popular methods using squared polydimethylsiloxane channels, this method allows for visualization of blood flow in channel geometries that resemble more the natural physiology of human blood vessels. With a custom designed enclosure, the microchannels were submerged in glycerol to reduce light refraction occurring during µPIV due to the thick walls of the glass channels. A method is proposed to correct the extracted velocity profiles from the µPIV to account for out-of-focus error. The customized elements of this method include: • The use of thick circular glass micro-channels, • a custom designed mounting solution for the channels on a glass slide for flow visualization, • a MATLAB code to correct velocity profile accounting for out-of-focus error.

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Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

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Nanoplastic pollution by atmospheric transport processes is a recently discovered environmental problem on a global scale that is attributed to the dispersion of aerosolized nanoplastics. However, knowledge about the basic physicochemical properties of aerosol nanoplastic particles is scarce. Here, we present experiments on the heterogeneous nucleation of supersaturated water vapor onto sub-10 nm polyethylene terephthalate (PET) seeds. We determined onset saturation ratios for the activation of PET seeds in comparison to the well-documented reference system of silver particles, resulting in lower onset saturation ratios of the PET seeds compared to silver seeds. By using different PET bulk materials for the generation of nanoparticles, we report a strong material dependence of the onset saturation ratios, pointing to a strong effect of additives from commodity plastics in heterogeneous nucleation. Moreover, our results show a strong dependence on nucleation temperature that might be of immediate atmospheric relevance. Our work can be considered as an initial step in airborne nanoplastic detection by condensation techniques, and we anticipate our study to serve as a basis for further research that will eventually allow assessing the impact of nanoplastic dispersion on atmospheric processes.

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Tongue diagnosis in Kampo medicine is considered to be a diagnostic method that can provide information about a patient's constitution and medical condition. We have identified the following problems in tongue diagnosis: the influence of external environmental factors such as light source, room temperature, and dryness, as well as subjective factors that depend on the knowledge and experience of medical doctors. To overcome these problems and to support Kampo diagnosis, we developed the Tongue Image Analyzing System (TIAS). Regarding color, objective numerical values L*a*b* were measured and standardization and objectification were promoted. We introduce some of the progress that has been made over the past 15 years since the development of TIAS.

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Efficiently analyzing and classifying dynamically changing time series data remains a challenge. The main issue lies in the significant differences in feature distribution that occur between old and new datasets generated constantly due to varying degrees of concept drift, anomalous data, erroneous data, high noise, and other factors. Taking into account the need to balance accuracy and efficiency when the distribution of the dataset changes, we proposed a new robust, generalized incremental learning (IL) model ELM-KL-LSTM. Extreme learning machine (ELM) is used as a lightweight pre-processing model which is updated using the new designed evaluation metrics based on Kullback-Leibler (KL) divergence values to measure the difference in feature distribution within sliding windows. Finally, we implemented efficient processing and classification analysis of dynamically changing time series data based on ELM lightweight pre-processing model, model update strategy and long short-term memory networks (LSTM) classification model. We conducted extensive experiments and comparation analysis based on the proposed method and benchmark methods in several different real application scenarios. Experimental results show that, compared with the benchmark methods, the proposed method exhibits good robustness and generalization in a number of different real-world application scenarios, and can successfully perform model updates and efficient classification analysis of incremental data with varying degrees improvement of classification accuracy. This provides and extends a new means for efficient analysis of dynamically changing time-series data.

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Context: Tumor microenvironment is emerging as a critical factor for progression of breast cancer. Tumor-associated macrophages (TAMs) play an important role in promoting tumor growth. Aim: This study was aimed at correlation of number density (ND) of TAMs with invasive ductal carcinoma (IDC) grading utilizing an image morphometric technique. We also sought to compare the TAMs and ND in the tumoral area and stromal region. We also explored the relationship between the clinical and pathological prognostic parameters. Subjects and Methods: The study included 75 cases of IDC that had undergone modified radical mastectomy. The Institutional Ethics Committee approved the study. Samples were classified as Grade 1, 2, and 3. Cases were graded as per the modified Bloom and Richardson criterion. Mean with standard deviation was calculated for each group. We utilized CD68 and CD163 immunostained sections for determining the ND of TAMs. TAMs were evaluated using computerized digital photomicrograph system with image analyzing software. ND was defined as the number of TAMs in total number of TAMs in five high-power fields/total area of five fields. ND was calculated separately in tumor and tumor stroma (TS). Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2/neu (HER2/neu) were scored in accordance with recommendations. Ki-67 was scored as per the recommended guidelines. Statistical Analysis Used: Data were tabulated in Microsoft Excel. SPSS version 20.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis. To determine the relationship between macrophage density and clinicopathologic parameters, we used the independent t-test. To determine the differences in the parameters, analysis of variance (ANOVA) was utilized. Results: Age of the patients ranged from 34 to 58 years (mean: 55.5). One-way ANOVA between various grades of tumor indicating significant differences in terms of CD68 and CD163 densities in tumor and stroma (P < 0.0001). i.e., significant increased density of CD68 and CD163 was observed in Grade 3 tumor as compared to other two groups. A greater histological grade, ER, PR negative status, and a high Ki-67 index were all associated with TAM ND. There was no relation to HER2/neu status. Result of unpaired t-test indicates increased density in stroma as compared to tumor among various grades of IDC. Conclusions: We analyzed images with a software using photographs of the stained slides. This helped in quantitative analysis of TAMs on the CD68 and CD163 stained sections. This approach standardizes and reproducibly counts TAMs per unit area. We found significant difference between the number densities of TAMs in grades of invasive breast carcinoma. There were statistically significant differences in numerical densities of TAMs with ER, PR negativity, and Ki-67. There was no correlation with HER2/neu. Densities of CD68 and CD163 densities are more prevalent in TS as compared to intratumoral region.

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Background: The COVID-19 pandemic has caused significant stress and disruption for young people, likely leading to alterations in their mental health and neurodevelopment. In this context, it is not clear whether youth who lived through the pandemic and its shutdowns are comparable psychobiologically to their age- and sex-matched peers assessed before the pandemic. This question is particularly important for researchers who are analyzing longitudinal data that span the pandemic. Methods: In this study we compared carefully matched youth assessed before the pandemic (n=81) and after the pandemic-related shutdowns ended (n=82). Results: We found that youth assessed after the pandemic shutdowns had more severe internalizing mental health problems, reduced cortical thickness, larger hippocampal and amygdala volume, and more advanced brain age. Conclusions: Thus, not only does the COVID-19 pandemic appear to have led to poorer mental health and accelerated brain aging in adolescents, but it also poses significant challenges to researchers analyzing data from longitudinal studies of normative development that were interrupted by the pandemic.