Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.

We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (MIoU) of 0.70, a mean precision rate of 0.88, a mean recall rate of 0.75, a mean F1-score of 0.81, and an accuracy of 1.0, which are all better than those of only using a single-network model. The proposed method can be used by clinicians as a diagnostic tool assisting in the examination of TB lesions in CXR images.

Synthesis of selenophene-containing flavonols and 2-styrylchromones: Evaluation of their activities compared with selenophene-containing chalcones as potential anticancer agents.

Previously, we documented the synthesis and assessed the biological effects of chalcones containing selenium against HT-29 human colorectal adenocarcinoma cells, demonstrating their significant potential. As research on selenium-containing flavonoids remains limited, this article outlines our design and synthesis of three selenium-based flavonols and three 2-styrylchromones. We conducted evaluations of these compounds to determine their impact on human lung cancer cells (A549, H1975, CL1-0, and CL1-5) and their influence on normal lung fibroblast MRC5 cells. Additionally, we included selenium-based chalcones in our testing for comparative purposes. Our findings highlight that the simplest compound, designated as compound 1, exhibited the most promising performance among the tested molecules.

A nationwide case-referent study on elevated risks of adenocarcinoma lung cancer by long-term PM2.5 exposure in Taiwan since 1997.

BACKGROUND: The effects of long-term PM2.5 exposures since 1968 on adenocarcinoma lung cancer (AdLC) were not studied before. METHODS: This case-referent study used nationwide cancer registry data since 1997 and air pollution data since 1968 in Taiwan to estimate risks of 30-year PM2.5 exposures on AdLC. Cases were all AdLC, while references were all non-AdLC. Individuals' 30-year PM2.5 exposures were estimated by PM2.5 levels at their residence for 30 years prior their diagnosis dates. We applied multiple logistic regression analyses to estimate PM2.5 exposures on incidence rate ratios (IRRs) between cases and references, adjusting for sex, age, smoking, cancer stage, and EGFR mutation. RESULTS: Elevation in annual ambient PM2.5 concentrations since 1968 were associated with increase in annual age-adjusted AdLC incidence since 1997. AdLC incidences were higher among females, nonsmokers, the elderly aged above 65, cases of stages IIIB to IV, and EGFR mutation. Study subjects' PM2.5 exposures averaged at 33.7 ± 7.4 µg/m3 with 162 ± 130 high PM2.5 pollution days over 30 years. Multiple logistic models showed an increase in 10 µg/m3 of PM2.5 exposures were significantly associated with 1.044 of IRR between all AdLC and all non-AdLC cases during 2011-2020. Our models also showed that females and nonsmokers and adults less than 65 years had higher IRRs than their respective counterparts. Restricted analyses showed similar effects of PM2.5 exposures on IRRs between stage 0-IIIA and IIIB-IV cases and between EGFR+ and EGFR- cases. CONCLUSIONS: Long-term exposures to PM2.5 over 30 years were associated with elevated risks of AdLC against non-AdLC, regardless of gender, age, smoking status, cancer stage, or EGFR mutation.

Estimates of the global burden of cancer-related deaths attributable to residential exposure to petrochemical industrial complexes from 2020 to 2040.

The petrochemical industry is a major industrial emitter of greenhouse gas (CO2) and environmental pollution, posing health risks to nearby communities. Although previous studies have indicated that residents living near petrochemical industrial complexes are at a higher risk of cancer, they have focused on local or regional burdens. This study aimed to estimate the global cancer burden attributable to residential exposure to petrochemical industrial complexes. The geographical coordinates of petrochemical plants and oil refineries were retrieved and verified from published sources. The ArcGIS software and global population data were used to estimate the number of people living within specific distances (exposed population). The exposure time window was framed as ranging from 1992 to 2035, extending to the latest period of the exposure time window for all cancer types to estimate the attributable deaths between 2020 and 2040. The relative risk of cancer was estimated from 15 published studies. Population attributable fraction (PAF) method was used to estimate the risk of cancer attributable to residential exposure and calculate the number of cancer-related deaths. Our findings indicate that >300 million people worldwide will be estimated to live near petrochemical industrial complexes by 2040. The overall global burden of cancer-related deaths was 19,083 in 2020, and it is estimated to increase to 27,366 deaths by 2040. The region with the highest attributable cancer deaths due to exposure is the high-income region, which had 10,584 deaths in 2020 and is expected to reach 13,414 deaths by 2040. Residential exposure to petrochemical industrial complexes could contribute to global cancer deaths, even if the proportion is relatively small, and proactive measures are required to mitigate the cancer burdens among these residents. Enforcing emissions regulations, improving monitoring, educating communities, and fostering collaboration are vital to protecting residents' health.

A novel lectin receptor kinase gene, AtG-LecRK-I.2, enhances bacterial pathogen resistance through regulation of stomatal immunity in Arabidopsis.

The S-locus lectin receptor kinases (G-LecRKs) have been suggested as receptors for microbe/damage-associated molecular patterns (MAMPs/DAMPs) and to be involved in the pathogen defense responses, but the functions of most G-LecRKs in biotic stress response have not been characterized. Here, we identified a member of this family, G-LecRK-I.2, that positively regulates flg22- and Pseudomonas syringae pv. tomato (Pst) DC3000-induced stomatal closure. G-LecRK-I.2 was rapidly phosphorylated under flg22 treatment and could interact with the FLS2/BAK1 complex. Two T-DNA insertion lines, glecrk-i.2-1 and glecrk-i.2-2, had lower levels of reactive oxygen species (ROS) and nitric oxide (NO) production in guard cells, as compared with the wild-type Col-0, under Pst DC3000 infection. Also, the immunity marker genes CBP60g and PR1 were induced at lower levels under Pst DC3000 hrcC- infection in glecrk-i.2-1 and glecrk-i.2-2. The GUS reporter system also revealed that G-LecRK-I.2 was expressed only in guard cells. We also found that G-LecRK-I.2 could interact H+-ATPase AHA1 to regulate H+-ATPase activity in the guard cells. Taken together, our results show that G-LecRK-I.2 plays an important role in regulating stomatal closure under flg22 and Pst DC3000 treatments and in ROS and NO signaling specifically in guard cells.

Integrating high-throughput phenotypic profiling and transcriptomic analyses to predict the hepatosteatosis effects induced by per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFAS) is a large compound class (n > 12,000) that is extensively present in food, drinking water, and aquatic environments. Reduced serum triglycerides and hepatosteatosis appear to be the common phenotypes for different PFAS chemicals. However, the hepatosteatosis potential of most PFAS chemicals remains largely unknown. This study aims to investigate PFAS-induced hepatosteatosis using in vitro high-throughput phenotype profiling (HTPP) and high-throughput transcriptomic (HTTr) data. We quantified the in vitro hepatosteatosis effects and mitochondrial damage using high-content imaging, curated the transcriptomic data from the Gene Expression Omnibus (GEO) database, and then calculated the point of departure (POD) values for HTPP phenotypes or HTTr transcripts, using the Bayesian benchmark dose modeling approach. Our results indicated that PFAS compounds with fully saturated C-F bonds, sulfur- and nitrogen-containing functional groups, and a fluorinated carbon chain length greater than 8 have the potential to produce biological effects consistent with hepatosteatosis. PFAS primarily induced hepatosteatosis via disturbance in lipid transport and storage. The potency rankings of PFAS compounds are highly concordant among in vitro HTPP, HTTr, and in vivo hepatosteatosis phenotypes (ρ = 0.60-0.73). In conclusion, integrating the information from in vitro HTPP and HTTr analyses can accurately project in vivo hepatosteatosis effects induced by PFAS compounds.

Exploring the Barriers and Facilitators of Mask-Wearing Behavior During the COVID-19 Pandemic in Taiwan, the United States, the Netherlands, and Haiti: A Qualitative Study.

OBJECTIVE: This study explored the barriers and facilitators of mask-wearing behaviors during the pandemic in Taiwan, the United States, the Netherlands, and Haiti. METHODS: Face-to-face interviews were conducted in Taiwan and online interviews were conducted with participants in the United States, the Netherlands, and Haiti. RESULTS: In general, the habit of wearing a mask before coronavirus disease 2019 (COVID-19) was reported by Taiwanese participants. Additionally, Taiwanese participants perceived that wearing a mask was a social responsibility during the pandemic, suggesting that the collectivistic context might influence mask-wearing behavior. Unlike the Taiwanese population, some people in the United States and the Netherlands were reluctant to wear masks due to perceived restrictions on their freedom. Participants from Haiti mentioned that people who wore masks encountered violence, bullying, and discrimination. The results of this study suggest that political leadership and mask mandates have a strong impact on people's mask-wearing behavior. CONCLUSIONS: These findings have valuable implications for the design of diverse behavioral interventions to enhance mask-wearing as part of infectious disease preparedness. Additionally, the findings from these countries offer valuable insights for the development of effective public health interventions to enhance society's resilience during the current pandemic and future infectious disease outbreaks.

Long-term PM2.5 exposure is associated with asthma prevalence and exhaled nitric oxide levels in children.

BACKGROUND: Exhaled nitric oxide concentration (FENO) is a marker of airway inflammation. This study aimed to evaluate the association of air pollution exposure with FENO levels and asthma prevalence with respiratory symptoms in school children. METHODS: We analyzed 4736 school children who reside in six townships near industrial areas in central Taiwan. We evaluated asthmatic symptoms, FENO, and conducted the environmental questionnaire. The personal exposure of PM2.5, NO, and SO2 was estimated using land-use regression models data on children's school and home addresses. RESULTS: Annual exposure to PM2.5 was associated with increased odds of physician-diagnosed asthma (OR = 1.595), exercise-induced wheezing (OR = 1.726), itchy eyes (OR = 1.417), and current nasal problems (OR = 1.334) (P < 0.05). FENO levels in the absence of infection were positively correlated with age, previous wheezing, allergic rhinitis, atopic eczema, near the road, and for children with high exposure to PM2.5 (P < 0.05). An increase of 1 µg/m3 PM2.5 exposure was significantly associated with a 1.0% increase in FENO levels for children after adjusting for potential confounding variables, including exposures to NO and SO2. CONCLUSIONS: Long-term exposures to PM2.5 posed a significant risk of asthma prevalence and airway inflammation in a community-based population of children. IMPACT: Annual exposure to PM2.5 was associated with increased odds of physician-diagnosed asthma and nasal problems and itchy eyes. Long-term exposures to PM2.5 were significantly associated with FENO levels after adjusting for potential confounding variables. This is first study to assess the association between FENO levels and long-term air pollution exposures in children near coal-based power plants. An increase of 1 µg/m3 annual PM2.5 exposure was significantly associated with a 1.0% increase in FENO levels. Long-term exposures to PM2.5 posed a significant risk of asthma prevalence and airway inflammation in a community-based population of children.

A Brain-Protective Sterol from Soft Coral Inhibits Lipopolysaccharide-Induced Matrix Metalloproteinase-9-Mediated Astrocytic Migration.

Matrix metalloproteinases (MMPs), which are proteolytic enzymes, promote blood-brain barrier (BBB) disruption, leading to neuronal damage and neuroinflammation. Among them, MMP-9 upregulation serves as an inflammatory biomarker in the central nervous system (CNS). Currently, the development of marine organism-derived bioactive compounds or metabolites as anti-inflammatory drugs has received considerable attention. The 9,11-secosteroid, 3ß,11-dihydroxy-9,11-secogorgost-5-en-9-one (4p3f), is a novel sterol compound extracted from the soft coral Sinularia leptoclado with potential anti-inflammatory activity. However, the effect of and potential for brain protection of 4p3f on brain astrocytes remain unclear. Herein, we used rat brain astrocytes (RBAs) to investigate the effects and signaling mechanisms of 4p3f on lipopolysaccharide (LPS)-induced MMP-9 expression via zymographic, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), Western blot, immunofluorescence staining, promoter-reporter, and cell migration analyses. We first found that 4p3f blocked LPS-induced MMP-9 expression in RBAs. Next, we demonstrated that LPS induced MMP-9 expression via the activation of ERK1/2, p38 MAPK, and JNK1/2, which is linked to the STAT3-mediated NF-κB signaling pathway. Finally, 4p3f effectively inhibited LPS-induced upregulation of MMP-9-triggered RBA cell migration. These data suggest that a novel sterol from soft coral, 4p3f, may have anti-inflammatory and brain-protective effects by attenuating these signaling pathways of MMP-9-mediated events in brain astrocytes. Accordingly, the soft coral-derived sterol 4p3f may emerge as a potential candidate for drug development or as a natural compound with neuroprotective properties.

Synthesis of 2-(pyridylvinyl)chromen-4-ones and their N-oxide analogs for assessment of their biological activities as anticancer agents.

2-Styrylchromones have been shown to possess a broad spectrum of biological activities. Replacing the carbon atom in 2-styrylchromones with a nitrogen atom in the benzene rings forms 2-(pyridylvinyl)chromen-4-ones (aza-2-styrylchromones). We have synthesized a series of novel 2-(pyridylvinyl)chromen-4-ones and their pyridine N-oxides to evaluate them as potential anticancer agents against human non-small-cell lung cancer cells (A549). Among the 18 synthesized molecules, compounds 18 and 8a exhibited comparable inhibitory effects to 5-fluorouracil and showed no toxicity against normal cells.

On the use of thermal forces to probe kinesin's response to force.

The stepping dynamics of cytoskeletal motor proteins determines the dynamics of cargo transport. In its native cellular environment, a molecular motor is subject to forces from several sources including thermal forces and forces ensuing from the interaction with other motors bound to the same cargo. Understanding how the individual motors respond to these forces can allow us to predict how they move their cargo when part of a team. Here, using simulation, we show that details of how the kinesin motor responds to small assisting forces-which, at the moment, are not experimentally constrained-can lead to significant changes in cargo dynamics. Using different models of the force-dependent detachment probability of the kinesin motor leads to different predictions on the run-length of the cargo they carry. These differences emerge from the thermal forces acting on the cargo and transmitted to the motor through the motor tail that tethers the motor head to the microtubule. We show that these differences appear for cargo carried by individual motors or motor teams, and use our findings to propose the use of thermal forces as a probe of kinesin's response to force in this otherwise inaccessible force regime.

Oridonin enhances cytotoxic activity of natural killer cells against lung cancer.

BACKGROUND: Oridonin is a Chinese herbal medicine exhibiting anti-tumor properties; however, its immune modulation capacity has yet to be elucidated. Our objective in this study was to determine whether oridonin enhances the anti-tumor activity of natural killer (NK) cells against lung cancer cells. METHODS: LDH-releasing assays were used to investigate the effects of oridonin on NK-92MI cell activity against lung cancer cells. Flow cytometry and real-time PCR were used to examine the effects of oridonin on degranulation markers, cytotoxic factors, activating receptors on NK-92MI cells, and ligands in lung cancer cells. Western blot analysis provided insight into the mechanisms underlying the observed effects. RESULTS: Oridonin enhanced the cytotoxic effects of NK-92MI cells against A549 lung cancer cells. This effect involved upregulating the expression of the degranulation marker CD107a and IFN-γ as well as activating receptors on NK cells and their ligand MICA/B. Oridonin also inhibited STAT3 phosphorylation in A549 cells and NK-92MI cells. A lung cancer mouse model confirmed the anti-tumor effects of oridonin and NK-92MI cells, wherein both treatments alone suppressed tumor growth. Oridonin was also shown to have a synergistic effect on the anti-tumor activity of NK-92MI cells. CONCLUSIONS: The ability of oridonin to enhance the cytotoxic effects of NK cells indicates its potential as a novel therapeutic agent for the treatment of lung cancer.

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson's Disease Model.

The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson's disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg-Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations.

Fear of falling as a mediator in the association between social frailty and health-related quality of life in community-dwelling older adults.

BACKGROUND: Social frailty is associated with Fear of Falling (FoF) and health-related quality of life (HrQoL). However, how social frailty simultaneously influences FoF and HrQoL remains unclear. The study aims to understand the links between social frailty, FoF, and HrQoL in older adults and the mediating role of FoF in the relations between social frailty and HrQoL. METHODS: In this cross-sectional survey, 1,933 community-dwelling older adults from Changhua County, Taiwan, were interviewed using a self-administrated questionnaire. In total, 1,251 participants with complete data were included for analysis. Data were analyzed using the SPSS PROCESS macro. A simple mediation was employed using social frailty as the independent variable, FoF as the mediator variable, and HrQoL as the outcome variable. RESULTS: Social frailty was associated with HrQoL and indirectly with HrQoL through FoF, and FoF was directly associated with HrQoL. Of the 5-item social frailty index, "going out less frequently" was correlated with HrQoL and indirectly with HrQoL through FoF. Individuals who felt unhelpful toward family or friends had the worst physical HrQoL and did not talk to someone daily had the most negative influence on mental HrQoL. CONCLUSIONS: Social frailty can directly and indirectly, through FoF decrease HrQoL. It also emphasizes the importance of social connectivity in reducing the risk of falls. This study points to the need for social connectivity and fall prevention programs as essential components of strategies to enhance the health and well-being of community-dwelling older adults.

Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features.

Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.

The ongoing fast-paced technology trend has brought forth ceaseless transformation. In this regard, cloud computing has long proven to be the paramount deliverer of services such as computing power, software, networking, storage, and databases on a pay-per-use basis. The cloud is a big proponent of the internet of things (IoT), furnishing the computation and storage requisite to address internet-of-things applications. With the proliferating IoT devices triggering a continual data upsurge, the cloud-IoT interaction encounters latency, bandwidth, and connectivity restraints. The inclusion of the decentralized and distributed fog computing layer amidst the cloud and IoT layer extends the cloud's processing, storage, and networking services close to end users. This hierarchical edge-fog-cloud model distributes computation and intelligence, yielding optimal solutions while tackling constraints like massive data volume, latency, delay, and security vulnerability. The healthcare domain, warranting time-critical functionalities, can reap benefits from the cloud-fog-IoT interplay. This research paper propounded a fog-assisted smart healthcare system to diagnose heart or cardiovascular disease. It combined a fuzzy inference system (FIS) with the recurrent neural network model's variant of the gated recurrent unit (GRU) for pre-processing and predictive analytics tasks. The proposed system showcases substantially improved performance results, with classification accuracy at 99.125%. With major processing of healthcare data analytics happening at the fog layer, it is observed that the proposed work reveals optimized results concerning delays in terms of latency, response time, and jitter, compared to the cloud. Deep learning models are adept at handling sophisticated tasks, particularly predictive analytics. Time-critical healthcare applications reap benefits from deep learning's exclusive potential to furnish near-perfect results, coupled with the merits of the decentralized fog model, as revealed by the experimental results.

Association between Hypertension and Stroke Recurrence as Modified by Pro-oxidant-Antioxidant Balance: A Multi-Center Study.

BACKGROUND: Hypertension and oxidative stress are involved in the pathophysiological mechanism of stroke. We aimed to investigate the modification impact of the pro-oxidant-anti-oxidant balance (PAB) on the association between hypertension and stroke recurrence (SR). METHODS: A cross-sectional design was conducted from December 2019 to December 2020 in 951 stroke patients in six hospitals across Vietnam. Hypertension was defined using antihypertensive medication or systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg. PAB was estimated using weighting methods based on smoking, drinking, and overweight/obesity with pro-oxidant capacity, diet quality, fruit intake, vegetable intake, and physical activity with antioxidant capacity. The higher PAB scores indicated a beneficial balance shifting toward antioxidant dominance. SR was diagnosed by neurologists. Moreover, sociodemographic and health conditions were included as covariates. Multiple logistic regression analyses were used to explore the associations and interactions. RESULTS: The hypertension and SR proportions were 72.8% and 17.5%, respectively. hypertension was associated with an increased SR likelihood (odds ratio (OR) = 1.93; p = 0.004), whereas a higher PAB score was associated with a lowered SR likelihood (OR = 0.87; p = 0.003). Moreover, hypertension interacting with every one-point increment of PAB was associated with a lowered SR likelihood (OR = 0.83; p = 0.022). CONCLUSIONS: The harmful impact of hypertension on SR could be alleviated by PAB. The interplay of health behaviors should be highlighted in the intervention strategies for stroke prevention.

Correction: Alkhaleefah et al. Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images. Cancers 2022, 14, 4030.

In the original publication [...].

A coarse-grained simulation model for colloidal self-assembly via explicit mobile binders.

Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet-droplet bonds, i.e., valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed 'colloidomers', which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites. We explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.

Challenges and Considerations in Identifying the Origin of Peritoneal Carcinomatosis: A Case Report.

Invasive lobular cancer (ILC) of the breast is the second most common type of invasive breast cancer. Clinical determination of the growth pattern of ILC of the breast is difficult. Furthermore, the ILC of the breast has a unique metastatic pattern that involves gastrointestinal and peritoneal sites. Our patient was initially misdiagnosed with left ovarian cancer based on the findings of positron emission tomography and computed tomography. Herein, we report a case of ILC of the breast presenting as peritoneal carcinomatosis. The ESMO Clinical Practice Guidelines for cancers of unknown primary sites were used in the diagnosis of the carcinoma of unknown primary origin. Image-guided biopsy and immunohistochemical staining are also useful in the diagnosis of these cancer types.