Nonmetastatic Axillary Lymph Nodes Have Distinct Morphology and Immunophenotype in Obese Patients with Breast Cancer at Risk for Metastasis.

Obese patients with breast cancer have worse outcomes than their normal weight counterparts, with a 50% to 80% increased rate of axillary nodal metastasis. Recent studies suggest a link between increased lymph node adipose tissue and breast cancer nodal metastasis. Further investigation into potential mechanisms underlying this link may reveal potential prognostic utility of fat-enlarged lymph nodes in patients with breast cancer. This study used a deep learning model to identify morphologic differences in nonmetastatic axillary nodes between obese, node-positive, and node-negative patients with breast cancer. The model was developed using nested cross-validation on 180 cases and achieved an area under the receiver operator characteristic curve of 0.67 in differentiating patients using hematoxylin and eosin-stained whole slide images. The morphologic analysis of the predictive regions showed an increased average adipocyte size (P = 0.004), increased white space between lymphocytes (P < 0.0001), and increased red blood cells (P < 0.001) in nonmetastatic lymph nodes of node-positive patients. Preliminary immunohistochemistry analysis on a subset of 30 patients showed a trend of decreased CD3 expression and increased leptin expression in fat-replaced axillary lymph nodes of obese, node-positive patients. These findings suggest a novel direction to further investigate the interaction between lymph node adiposity, lymphatic dysfunction, and breast cancer nodal metastases, highlighting a possible prognostic tool for obese patients with breast cancer.

Development and Validation of the Veterans Affairs Eosinophilic Esophagitis Cohort.

BACKGROUND & AIMS: Gaps remain in understanding the epidemiology of eosinophilic esophagitis (EoE). Our aim was to identify and validate a national cohort of individuals with EoE using Veterans Health Administration (VHA) data. METHODS: We used 2 validation strategies to develop algorithms that identified adults with EoE between 1999 and 2020. The first validation strategy applied International Classification of Diseases (ICD) code algorithms to a base cohort of individuals who underwent esophagogastroduodenoscopy with esophageal biopsy specimens. The second applied ICD code algorithms to a base cohort of all individuals in the VHA. For each ICD algorithm applied, a random sample of candidate EoE cases and non-EoE controls were selected and the charts were reviewed manually by a blinded reviewer. Each algorithm was modified iteratively until the prespecified diagnostic accuracy end point (95% confidence lower bound for a positive predictive value [PPV], >88%) was achieved. We compiled individuals from each strategy's maximum performance algorithm to construct the Veterans Affairs Eosinophilic Esophagitis Cohort. RESULTS: The maximum performance algorithm from the first validation strategy included 2 or more ICD code encounters for EoE separated by more than 30 days and achieved a 93.3% PPV (lower bound, 88.1%) for identifying true EoE cases. The maximum performance algorithm from the second validation strategy included 4 or more ICD code encounters for EoE in which 2 codes were separated by at least 30 days, and similarly achieved a 93.3% PPV (lower bound, 88.1%). Combining both strategies yielded 6637 individuals, which comprised the Veterans Affairs Eosinophilic Esophagitis Cohort. CONCLUSIONS: We developed and validated 2 highly accurate coding algorithms for EoE and established a nationwide VHA cohort of adults with EoE for future studies.

A molecularly imprinted polypyrrole electrochemiluminescence sensor based on a novel zinc-based metal-organic framework and chitosan for determination of enrofloxacin.

Nowadays, bacterial resistance caused by the abuse of antibiotics has become a worldwide problem. In this work, a quinolone antibiotic, enrofloxacin (ENR), was rapidly monitored by combining a selective molecular imprinting polymer (MIP) with the electrochemiluminescence (ECL) method. Zn-PTC, a novel zinc-based metal-organic framework (MOF) that has a large specific surface area and ultra-high luminous efficiency, was used as the ECL luminophore. Chitosan (CHIT) was used to contact the specific surface area of molecularly imprinted polymer films and further improved the detection sensitivity. Subsequently, the molecularly imprinted polypyrrole was electropolymerized on the surface of the Zn-PTC and CHIT modified glassy carbon electrode (GCE). The specific sites that could target recombining ENR were shaped on the surface of MIP after extracting the ENR templates. The specific concentrations of ENR could be detected according to the difference in ECL intensity (ΔECL) between the eluting and rebinding of ENR. After optimization, a good linear response of ΔECL and a logarithm of specific ENR concentrations could be obtained in the range of 1.0 × 10-12-1.0 × 10-4 mol L-1, with a detection limit of 9.3 × 10-13 mol L-1 (S/N = 3). Notably, this study provided a rapid, convenient, and cheap method for the detection of ENR in actual samples.

Examining Online Behaviors of Adult-Child and Spousal Caregivers for People Living With Alzheimer Disease or Related Dementias: Comparative Study in an Open Online Community.

BACKGROUND: Alzheimer disease or related dementias (ADRD) are severe neurological disorders that impair the thinking and memory skills of older adults. Most persons living with dementia receive care at home from their family members or other unpaid informal caregivers; this results in significant mental, physical, and financial challenges for these caregivers. To combat these challenges, many informal ADRD caregivers seek social support in online environments. Although research examining online caregiving discussions is growing, few investigations have distinguished caregivers according to their kin relationships with persons living with dementias. Various studies have suggested that caregivers in different relationships experience distinct caregiving challenges and support needs. OBJECTIVE: This study aims to examine and compare the online behaviors of adult-child and spousal caregivers, the 2 largest groups of informal ADRD caregivers, in an open online community. METHODS: We collected posts from ALZConnected, an online community managed by the Alzheimer's Association. To gain insights into online behaviors, we first applied structural topic modeling to identify topics and topic prevalence between adult-child and spousal caregivers. Next, we applied VADER (Valence Aware Dictionary for Sentiment Reasoning) and LIWC (Linguistic Inquiry and Word Count) to evaluate sentiment changes in the online posts over time for both types of caregivers. We further built machine learning models to distinguish the posts of each caregiver type and evaluated them in terms of precision, recall, F1-score, and area under the precision-recall curve. Finally, we applied the best prediction model to compare the temporal trend of relationship-predicting capacities in posts between the 2 types of caregivers. RESULTS: Our analysis showed that the number of posts from both types of caregivers followed a long-tailed distribution, indicating that most caregivers in this online community were infrequent users. In comparison with adult-child caregivers, spousal caregivers tended to be more active in the community, publishing more posts and engaging in discussions on a wider range of caregiving topics. Spousal caregivers also exhibited slower growth in positive emotional communication over time. The best machine learning model for predicting adult-child, spousal, or other caregivers achieved an area under the precision-recall curve of 81.3%. The subsequent trend analysis showed that it became more difficult to predict adult-child caregiver posts than spousal caregiver posts over time. This suggests that adult-child and spousal caregivers might gradually shift their discussions from questions that are more directly related to their own experiences and needs to questions that are more general and applicable to other types of caregivers. CONCLUSIONS: Our findings suggest that it is important for researchers and community organizers to consider the heterogeneity of caregiving experiences and subsequent online behaviors among different types of caregivers when tailoring online peer support to meet the specific needs of each caregiver group.

A novel molecularly imprinting polypyrrole electrochemiluminescence sensor based on MIL-101-g-C3N4 for supersensitive determination of ciprofloxacin.

Ciprofloxacin (CIP), a quinolone antibiotic, was rapidly and sensitively detected by integrating the molecularly imprinted polymer (MIP) with an ultra-sensitive electrochemiluminescence (ECL) method. g-C3N4, a typical polymer semiconductor, exhibited outstanding ECL efficiency and excellent ECL stability after combining with an iron-based metal-organic framework (MIL-101). Subsequently, the molecularly imprinted polypyrrole was electropolymerized on the composites of MIL-101-g-C3N4 modified glassy carbon electrode (GCE). The specific sites that could target rebinding the CIP molecules were formed on the surface of MIP after extracting the CIP templates. The determination of specific concentrations of CIP could be realized according to the difference in ECL intensity (△ECL) between the eluting and rebinding of the CIP. Under optimal conditions, a good linear response of △ECL and the logarithm of CIP concentrations was obtained in the range 1.0 × 10-9 ~ 1.0 × 10-5 mol/L, with a detection limit of 4.5 × 10-10 mol/L (S/N = 3) (the working potential was -1.8 ~ 0 V). The RSD of all points in the calibration plot was less than 5.0% and the real samples recovery was between 98.0 and 104%. This paper displays satisfactory selectivity and sensitivity, providing a rapid, convenient, and cheap method for the determination of CIP in real samples.

A novel three-dimensional molecularly imprinted polypyrrole electrochemical sensor based on MOF derived porous carbon and nitrogen doped graphene for ultrasensitive determination of dopamine.

Herein, a novel molecular imprinting polypyrrole electrochemical sensor was fabricated based on a zirconia and carbon core-shell structure (ZrO2@C) and a nitrogen-doped graphene (NPG) modified glassy carbon electrode (GCE) for ultrasensitive recognition of dopamine (DA). The NPG was prepared by a sacrificial-template-assisted pyrolysis method and ZrO2@C was synthesized via annealing treatment of a zirconium-based metal-organic framework (UiO-66). A convenient electropolymerization method was used to prepare the pyrrole (Py) conductive molecularly imprinted polymer (MIP) in the presence of DA. The elution process of DA was performed by a simple overoxidation process under alkaline conditions. Differential pulse voltammetry (DPV) was used to assess the electrochemical performance of the sensors. The MIP-based electrochemical sensor with specific binding sites could be used for selective recognition of DA. Under the optimal conditions, the linear range of such a sensor was 5.0 × 10-9-1.0 × 10-4 mol L-1 and the detection limit was 3.3 × 10-10 mol L-1 (S/N = 3). This sensor exhibited suitable selectivity, stability, and reproducibility, which suggested that it could be a promising candidate for rapid diagnostic methods in dopamine investigations.

Fat-enlarged axillary lymph nodes are associated with node-positive breast cancer in obese patients.

PURPOSE: Obesity associated fat infiltration of organ systems is accompanied by organ dysfunction and poor cancer outcomes. Obese women demonstrate variable degrees of fat infiltration of axillary lymph nodes (LNs), and they are at increased risk for node-positive breast cancer. However, the relationship between enlarged axillary nodes and axillary metastases has not been investigated. The purpose of this study is to evaluate the association between axillary metastases and fat-enlarged axillary nodes visualized on mammograms and breast MRI in obese women with a diagnosis of invasive breast cancer. METHODS: This retrospective case-control study included 431 patients with histologically confirmed invasive breast cancer. The primary analysis of this study included 306 patients with pre-treatment and pre-operative breast MRI and body mass index (BMI) > 30 (201 node-positive cases and 105 randomly selected node-negative controls) diagnosed with invasive breast cancer between April 1, 2011, and March 1, 2020. The largest visible LN was measured in the axilla contralateral to the known breast cancer on breast MRI. Multivariate logistic regression models were used to assess the association between node-positive status and LN size adjusting for age, BMI, tumor size, tumor grade, tumor subtype, and lymphovascular invasion. RESULTS: A strong likelihood of node-positive breast cancer was observed among obese women with fat-expanded lymph nodes (adjusted OR for the 4th vs. 1st quartile for contralateral LN size on MRI: 9.70; 95% CI 4.26, 23.50; p < 0.001). The receiver operating characteristic curve for size of fat-enlarged nodes in the contralateral axilla identified on breast MRI had an area under the curve of 0.72 for predicting axillary metastasis, and this increased to 0.77 when combined with patient and tumor characteristics. CONCLUSION: Fat expansion of axillary lymph nodes was associated with a high likelihood of axillary metastases in obese women with invasive breast cancer independent of BMI and tumor characteristics.

An electrochemiluminescence aptasensor for diethylstilbestrol assay based on resonance energy transfer between Ag3PO4-Cu-MOF(II) and silver nanoparticles.

In this work, a novel electrochemiluminescence (ECL) aptasensor based on the resonance energy transfer (RET) effect between Ag3PO4-Cu-MOF (ii) and silver nanoparticles (Ag NPs) is proposed. The ECL emission spectra of Ag3PO4-Cu-MOF and the ultraviolet absorption spectra of Ag NPs showed a good spectral overlap. Based on this, we designed an "on-off-on" ECL sensing strategy for the sensitive and specific detection of diethylstilbestrol (DES). Under the optimal conditions, the linear range of the sensor for DES detection was 1.0 × 10-12-1.0 × 10-4 M, with a detection limit of 7.2 × 10-13 M (S/N = 3). The method showed simple and fast operation, high sensitivity and selectivity, a strong anti-interference ability and good stability. More importantly, the developed aptasensor exhibited excellent recognition towards residual DES in actual water samples. The sensor has superior measurement capability and potential application value in the field of environment water quality monitoring.

Novel use of structural equation modeling to examine the development of a framework of patient-centered two-way referral systems for building digital subjective well-being healthcare: A cross-sectional survey in Central China.

Background: Digital health technologies are progressively assuming significant roles in aspects encompassing in-hospital management, patient-centered design, and tiered referral systems. Nevertheless, current studies do not involve exploration into the potential value and mechanisms of digital health in a patient-centered context. This study aimed to explore the development of a framework of comprehensive, evidence-based digital health technologies for the construction of welfare-oriented healthcare. Methods: From March to June 2023, a cross-sectional online study was performed, involving 335 respondents with prior referral experiences hailing from the Central China region. Data on welfare-oriented healthcare factors (clinical pathway management, medical structure configuration, healthcare service accessibility, two-way referrals) underwent factor analysis in advance, and correlation between these factors and their association with two-way referrals was evaluated by testing for direct and indirect (mediating) effects. Results: Firstly, there existed a significant positive correlation between integrative medical indicators and welfare-centered healthcare (ß = 0.02-0.16, p < 0.05). Furthermore, two-way referral had an direct association with integrative medical parameters and the welfare healthcare service system (ß = 0.15-0.31, p < 0.05), but exerted a partial mediatory function in the welfare healthcare service system (ß = 0.005-0.021, α < 0.05). Two-way referrals partially mediate the integrated medical indicators, mainly through direct effects, while also providing complementary support. Clinical pathways, medical structure, and accessibility are closely linked to welfare healthcare and significantly influence healthcare quality. Thus, improving these factors should be prioritized. Conclusion: This study proposes a method combining integrated evaluation indicators with pathway mechanism design. This pathway mechanism design includes key steps such as patient registration, information extraction, hospital allocation or referral, diagnosis and treatment, rehabilitation plan monitoring, service feedback, and demand resolution. This design aims to change patients' intentions in seeking healthcare, thereby increasing their acceptance of bidirectional referrals, and ultimately enhancing the effectiveness and realization of welfare healthcare.

Carboxyl graphene modified PEDOT:PSS organic electrochemical transistor for in situ detection of cancer cell morphology.

Circulating tumor cells in human peripheral blood play an important role in cancer metastasis. In addition to the size-based and antibody-based capture and separation of cancer cells, their electrical characterization is important for rare cell detection, which can prove fatal in point-of-care testing. Herein, an organic electrochemical transistor (OECT) biosensor made of solution-gated carboxyl graphene mixed with PEDOT:PSS for the detection of cancer cells in situ is reported. Carboxyl graphene was used in this work to modulate cancer cell morphology, which differs significantly from normal blood cells, to achieve rare cancer cell detection. When the concentration of carboxyl graphene mixed in PEDOT:PSS was increased from 0 to 5 mg mL-1, the cancer cell surface area increased from 218 µm2 to 530 µm2, respectively. A change in cell morphology was also detected by the OECT. Negative charges in the cancer cells induced a positive shift in gate voltage, which was approximately 40 mV for spherical-shaped cells. When the cell surface area increased, transfer curves of transistor revealed a negative shift in gate voltage. Therefore, the sensor can be used for in situ detection of cancer cell morphology during the cell capture process, which can be used to identify whether the captured cells are deformable.

Perceptions and Utilization of Online Peer Support Among Informal Dementia Caregivers: Survey Study.

Background: Informal dementia caregivers are those who care for a person living with dementia and do not receive payment (eg, family members, friends, or other unpaid caregivers). These informal caregivers are subject to substantial mental, physical, and financial burdens. Online communities enable these caregivers to exchange caregiving strategies and communicate experiences with other caregivers whom they generally do not know in real life. Research has demonstrated the benefits of peer support in online communities, but this research is limited, focusing merely on caregivers who are already online community users. Objective: We aimed to investigate the perceptions and utilization of online peer support through a survey. Methods: Following the Andersen and Newman Framework of Health Services Utilization and using REDCap (Research Electronic Data Capture), we designed and administered a survey to investigate the perceptions and utilization of online peer support among informal dementia caregivers. Specifically, we collected types of information that influence whether an informal dementia caregiver accesses online peer support: predisposing factors, which refer to the sociocultural characteristics of caregivers, relationships between caregivers and people living with dementia, and belief in the value of online peer support; enabling factors, which refer to the logistic aspects of accessing online peer support (eg, eHealth literacy and access to high-speed internet); and need factors, which are the most immediate causes of seeking online peer support. We also collected data on caregivers' experiences with accessing online communities. We distributed the survey link on November 14, 2022, within two online locations: the Alzheimer's Association website (as an advertisement) and ALZConnected (an online community organized by the Alzheimer's Association). We collected all responses on February 23, 2023, and conducted a regression analysis to identifyn factors that were associated with accessing online peer support. Results: We collected responses from 172 dementia caregivers. Of these participants, 140 (81.4%) completed the entire survey. These caregivers were aged 19 to 87 (mean 54, SD 13.5) years, and a majority were female (123/140, 87.9%) and White (126/140, 90%). Our findings show that the behavior of accessing any online community was significantly associated with participants' belief in the value of online peer support (P=.006). Moreover, of the 40 non-online community caregivers, 33 (83%) had a belief score above 24-the score that was assigned when a neutral option was selected for each belief question. The most common reasons for not accessing any online community were having no time to do so (14/140, 10%) and having insufficient online information-searching skills (9/140, 6.4%). Conclusions: Our findings suggest that online peer support is valuable, but practical strategies are needed to assist informal dementia caregivers who have limited time or online information-searching skills.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). Conclusions: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare.

Background: The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators. Objective: This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare. Methods: We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns. Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research. Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

The Hidden Patient Connections: Predicting Hormonal Therapy Medication Discontinuation Using Hypergraph Neural Network on Clinical Communications.

Hormonal therapy is an important adjuvant treatment for breast cancer patients, but medication discontinuation of such therapy is not uncommon. The goal of this paper is to conduct research on the modeling of clinic communications, which have shown value in understanding medication discontinuation, to predict the discontinuation of hormonal therapy medications. Notably, we leveraged the Hypergraph Neural Network to capture the hidden connections of patients that were inferred from clinical communications. Combining the content of clinical communications as well as the demographics, insurance, and cancer stage information, our model achieved an AUC of 67.9%, which significantly outperformed other baselines such as Graph Convolutional Network (65.3%), Random Forest (62.7%), and Support Vector Machine (62.8%). Our study suggested that incorporating the hidden patient connections encoded in clinical communications into prediction models could boost their performance. Future research would consider combining structured medical records and clinical communications to better predict medication discontinuation.

Fatigue, Pain, and Medication: Mining Online Posts Regarding Rheumatoid Arthritis From Reddit.

Rheumatoid arthritis (RA), a chronic and systemic autoimmune disease that primarily attacks the joints around the body, is affecting a large number of people worldwide through severe symptoms and complications. Therefore, it is crucial to understand these patients' problems and support needs such that effective strategies or solutions can be made to improve their long-term treatment experience. In this paper, we present an in-depth study that is based on the structural topic model to uncover the themes and concerns in online RA posts from Reddit, an American social news aggregation, content rating, and discussion website. In addition, we compared the topic prevalence differences before and after the COVID-19 pandemic to understand the impact of the pandemic on these online users. This study demonstrates the potential of using text-mining techniques on social media data to learn the treatment experiments of RA patients.

CT characterization of retractor related liver injuries after pancreaticoduodenectomy: Retrospective analysis of a single institution experience.

PURPOSE: Retractor related liver injuries (RRLI) are reported after upper gastrointestinal tract surgeries; most commonly laparoscopic cholecystectomy and gastric surgeries. The aim of this study was to characterize the incidence, identification, type, severity, clinical features and risk factors for RRLI after open and robotic pancreaticoduodenectomy. METHODS: A 6-year retrospective study of 230 patients was performed. Clinical data was extracted from the electronic medical record. Post-operative imaging was reviewed and graded using the American Association for the Surgery of Trauma (AAST) liver injury scale. RESULTS: 109 patients met eligibility criteria. RRLI occurred in 23/109 (21.1%), with a higher incidence in the robotic/combinedapproach (4/9) compared with open (19/100). Most common injury was an intraparenchymal hematoma (56.5%), grade II (78.3%), located in segments II/III (77%). 39.1% of injuries were not reported on the CT interpretation. There was a statistically significant elevation of postoperative AST/ALT in the RRLI group [median AST 219.5 vs. 72.0 (p < 0.001), ALT 203.0 vs. 69.0 (p < 0.001)]. Trends toward lower preoperative platelet counts and longer operations were observed in the RRLI group. No significant difference in hospital length of stay or post-operative pain scores were noted. CONCLUSION: RRLI occurred frequently after pancreaticoduodenectomy, however most injuries were low grade and the only clinical significance was a transient increase in transaminases. A trend toward higher injury rates was observed in robotic cases. In this population, RRLI was often unrecognized on postoperative imaging.

Non-Metastatic Axillary Lymph Nodes Have Distinct Morphology and Immunophenotype in Obese Breast Cancer patients at Risk for Metastasis.

Obese patients have worse breast cancer outcomes than normal weight women including a 50% to 80% increased rate of axillary nodal metastasis. Recent studies have shown a potential link between increased lymph node adipose tissue and breast cancer nodal metastasis. Further investigation into potential mechanisms underlying this link may reveal potential prognostic utility of fat-enlarged lymph nodes in breast cancer patients. In this study, a deep learning framework was developed to identify morphological differences of non-metastatic axillary nodes between node-positive and node-negative obese breast cancer patients. Pathology review of the model-selected patches found an increase in the average size of adipocytes (p-value=0.004), an increased amount of white space between lymphocytes (p-value<0.0001), and an increased amount of red blood cells (p-value<0.001) in non-metastatic lymph nodes of node-positive breast cancer patients. Our downstream immunohistology (IHC) analysis showed a decrease of CD3 expression and increase of leptin expression in fat-replaced axillary lymph nodes in obese node-positive patients. In summary, our findings suggest a novel direction to further investigate the crosstalk between lymph node adiposity, lymphatic dysfunction, and breast cancer nodal metastases.

Fabrication of PEDOT:PSS-based solution gated organic electrochemical transistor array for cancer cells detection.

Organic electrochemical transistor (OECT) was applied in chemical and biological sensing. In this work, we developed a simple and repeatable method to fabricate OECT array, which had been successfully used to detect cancer cells. PEDPT:PSS conductive film between source and drain electrodes were patterned through photolithography, which can achieve uniform devices with same electrical characterization. When MCF-7 cancer cells are captured on the PEDOT:PSS surface via specifical antibody, the transfer characteristic of OECT shifts to higher gate electrode voltage due to the electrostatic interaction between cancer cells and device. The effective gate voltage shift can reach about 63 mV when the concentration of cancer cells increased to 5000. The shift of effective gate voltage is related to the cancer cell morphology, which is increased in the first 1 h and decreased when the capture time was larger than 1 h. The device of OECT array can increase the sample flux and make the detection result more accurate. It is expected that OECT array will have promising practical applications in single cancer cell detection in the future.

Hydrogen Sulfide Enhances Browning Repression and Quality Maintenance in Fresh-Cut Peaches via Modulating Phenolic and Amino Acids Metabolisms.

Effects of hydrogen sulfide (H2S) on the browning and quality maintenance of fresh-cut peach fruit were studied. The results showed that H2S treatment repressed the development of surface browning, suppressed the increase in respiration rate and weight loss, and delayed the decline of firmness while soluble solids content (SSC) and microbial growth were unaffected during storage. H2S treatment maintained higher contents of phenolic compounds, especially neo-chlorogenic acid, catechin, and quercetin, and delayed the degradation of phenolic compounds by enhancing the activities of phenolic biosynthesis-related enzymes and inhibiting the oxidative activities of polyphenol oxidase (PPO) in comparison with control. Moreover, H2S stimulated the accumulation of amino acids and their derivatives including proline, γ-aminobutyric acid (GABA), and polyamines (PAs) via enhancing biosynthesis and repressing degradation compared to control. These results suggested that H2S treatment enhanced the accumulation of phenolic, amino acids, and their derivatives by modulating phenolic and amino acids metabolisms, which contributed to the higher antioxidant activity and membrane integrity maintenance, ultimately repressing browning development and maintaining the quality. Therefore, the current study speculated that H2S might be a promising approach for browning inhibition and quality maintenance in fresh-cut peach fruit.