Does it matter who harmed whom? A cross-cultural study of moral judgments about harm by and to insiders and outsiders.

This cross-cultural study compared judgments of moral wrongness for physical and emotional harm with varying combinations of in-group vs. out-group agents and victims across six countries: the United States of America (N = 937), the United Kingdom (N = 995), Romania (N = 782), Brazil (N = 856), South Korea (N = 1776), and China (N = 1008). Consistent with our hypothesis we found evidence of an insider agent effect, where moral violations committed by outsider agents are generally considered more morally wrong than the same violations done by insider agents. We also found support for an insider victim effect where moral violations that were committed against an insider victim generally were seen as more morally wrong than when the same violations were committed against an outsider, and this effect held across all countries. These findings provide evidence that the insider versus outsider status of agents and victims does affect moral judgments. However, the interactions of these identities with collectivism, psychological closeness, and type of harm (emotional or physical) are more complex than what is suggested by previous literature. Supplementary information: The online version contains supplementary material available at 10.1007/s12144-023-04986-3.

Dual-Mode Graphene Field-Effect Transistor Biosensor with Isothermal Nucleic Acid Amplification.

Despite a substantial increase in testing facilities during the pandemic, access remains a major obstacle, particularly in low-resource and remote areas. This constraint emphasizes the need for high-throughput potential point-of-care diagnostic tools in environments with limited resources. Loop-mediated isothermal amplification (LAMP) is a promising technique, but improvements in sensitivity are needed for accurate detection, especially in scenarios where the virus is present in low quantities. To achieve this objective, we present a highly sensitive detection approach of a dual-mode graphene-based field-effect transistor (G-FET) biosensor with LAMP. The G-FET biosensor, which has a transparent graphene microelectrode array on a glass substrate, detects LAMP products in less than 30 min using both observable color changes and Dirac point voltage measurements, even in samples with low viral concentrations. This dual-mode G-FET biosensor emerges as a potential alternative to conventional RT-PCR for severe acute respiratory syndrome-associated coronavirus (SARS-CoV)-2 detection or point-of-care testing, particularly in resource-constrained scenarios such as developing countries. Moreover, its capacity for colorimetric detection with the naked eye enhances its applicability in diverse settings.

Prokineticin 2 is a catabolic regulator of osteoarthritic cartilage destruction in mouse.

BACKGROUND: Our preliminary study indicates that the multi-functional protein, prokineticin 2 (Prok2), is upregulated in osteoarthritic (OA) chondrocytes as a target of the hypoxia-inducible factor (HIF)-2α. This study aims to elucidate the potential roles of Prok2 in OA. METHODS: Prok2 expression was assessed through microarray analysis in chondrocytes and confirmed via immunostaining in OA cartilage. Experimental OA was induced through destabilization of the medial meniscus (DMM). Functions of Prok2 were assessed by adenoviral overexpression, intra-articular (IA) injection of recombinant Prok2 (rProk2), and knockdown of Prok2 in joint tissues. We also explored the potential utility of Prok2 as an OA biomarker using enzyme-linked immunosorbent assay (ELISA). RESULTS: HIF-2α upregulated Prok2, one of the prokineticin signaling components, in OA chondrocytes of mice and humans. Adenoviral overexpression of Prok2 in chondrocytes and cartilage explants, as well as the application of rProk2, led to an upregulation of matrix metalloproteinase (MMP)3 and MMP13. Consistently, the overexpression of Prok2 in joint tissues or IA injection of rProk2 exacerbated cartilage destruction and hindpaw mechanical allodynia induced by DMM. However, the knockdown of Prok2 in joint tissues did not significantly affect DMM-induced cartilage destruction. Additionally, despite being a secreted protein, the serum levels of Prok2 in OA mice and human OA patients were found to be below the range detected by ELISA. CONCLUSION: The upregulation of Prok2 exacerbates OA cartilage destruction and hindpaw mechanical allodynia. However, its knockdown is not sufficient to inhibit experimental OA and Prok2 is not a potential candidate serum biomarker of OA.

Gold nanostructures modified carbon-based electrode enhanced with methylene blue for point-of-care COVID-19 tests using isothermal amplification.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) envelope (E) and RNA-dependent RNA polymerase (RdRP) genes were detected via electrochemical measurements using a screen-printed carbon electrode (SPCE) (3-electrode system) coupled with a battery-operated thin-film heater based on the loop-mediated isothermal amplification (LAMP) technique. The working electrodes of the SPCE sensor were decorated with synthesized gold nanostars (AuNSs) to obtain a large surface area and improve sensitivity. The LAMP assay was enhanced using a real-time amplification reaction system to detect the optimal target genes (E and RdRP) of SARS-CoV-2. The optimized LAMP assay was performed with diluted concentrations (from 0 to 109 copies) of the target DNA using 30 µM of methylene blue as a redox indicator. Target DNA amplification was conducted for 30 min at a constant temperature using a thin-film heater, and the final amplicon electrical signals were detected based on cyclic voltammetry curves. Our electrochemical LAMP analysis of SARS-CoV-2 clinical samples showed an excellent correlation with the Ct value of real-time reverse transcriptase-polymerase chain reaction, indicating successful validation of results. A linear relationship between the peak current response and the amplified DNA was observed for both genes. The AuNS-decorated SPCE sensor with the optimized LAMP primer enabled accurate analysis of both SARS-CoV-2-positive and -negative clinical samples. Therefore, the developed device is suitable for use as a point-of-care test DNA-based sensor for the diagnosis of SARS-CoV-2.

Gut Microbial Genes and Metabolism for Methionine and Branched-Chain Amino Acids in Diabetic Nephropathy.

Diabetic mellitus nephropathy (DMN) is a serious complication of diabetes and a major health concern. Although the pathophysiology of diabetes mellitus (DM) leading to DMN is uncertain, recent evidence suggests the involvement of the gut microbiome. This study aimed to determine the relationships among gut microbial species, genes, and metabolites in DMN through an integrated clinical, taxonomic, genomic, and metabolomic analysis. Whole-metagenome shotgun sequencing and nuclear magnetic resonance metabolomic analyses were performed on stool samples from 15 patients with DMN and 22 healthy controls. Six bacterial species were identified to be significantly elevated in the DMN patients after adjusting for age, sex, body mass index, and estimated glomerular filtration rate (eGFR). Multivariate analysis found 216 microbial genes and 6 metabolites (higher valine, isoleucine, methionine, valerate, and phenylacetate levels in the DMN group and higher acetate levels in the control group) that were differentially present between the DMN and control groups. Integrated analysis of all of these parameters and clinical data using the random-forest model showed that methionine and branched-chain amino acids (BCAAs) were among the most significant features, next to the eGFR and proteinuria, in differentiating the DMN group from the control group. Metabolic pathway gene analysis of BCAAs and methionine also revealed that many genes involved in the biosynthesis of these metabolites were elevated in the six species that were more abundant in the DMN group. The suggested correlation among taxonomic, genetic, and metabolic features of the gut microbiome would expand our understanding of gut microbial involvement in the pathogenesis of DMN and may provide potential therapeutic targets for DMN. IMPORTANCE Whole metagenomic sequencing uncovered specific members of the gut microbiota associated with DMN. The gene families derived from the discovered species are involved in the metabolic pathways of methionine and branched-chain amino acids. Metabolomic analysis using stool samples showed increased methionine and branched-chain amino acids in DMN. These integrative omics results provide evidence of the gut microbiota-associated pathophysiology of DMN, which can be further studied for disease-modulating effects via prebiotics or probiotics.

Thyroid storm in a pregnant woman with COVID-19 infection: A case report and review of literatures.

BACKGROUND: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been found to be responsible for the recent global pandemic known as coronavirus disease 2019 (COVID-19). SARS-CoV-2 infections not only result in significant respiratory symptoms but also cause several extrapulmonary manifestations, such as thrombotic complications, myocardial dysfunction and arrhythmia, thyroid dysfunction, acute kidney injury, gastrointestinal symptoms, neurological symptoms, ocular symptoms, and dermatological complications. We present the first documented case of thyroid storm in a pregnant woman precipitated by SARS-CoV-2. CASE SUMMARY: A 42-year-old multiparous woman at 35 + 2 wk of gestation visited the emergency room (ER) with altered mentation, seizures, tachycardia, and high fever. The patient showed no remarkable events in the prenatal examination, and the nasopharyngeal COVID-19 polymerase chain reaction (PCR) test was positive two days before the ER visit. The results of laboratory tests, such as liver function test, serum electrolytes, blood glucose, blood urea nitrogen, and creatinine, were all within the normal ranges. However, the thyroid function test showed hyperthyroidism, and the nasopharyngeal COVID-19 PCR test was positive, as expected. No specific findings were observed on the brain computed tomography, and there were no signs of lateralization on neurological examination. Fetal heartbeat and movement were good, and there were no significant uterine contractions. The initial impression was atypical eclampsia. However, the patient's condition worsened, and a cesarean section was performed under general anesthesia; a healthy boy was delivered, and 12 h after delivery, the patient's seizures disappeared and consciousness was restored. The patient was referred to an endocrinologist for hyperthyroidism, and a thyroid storm with Graves' disease was diagnosed. Here, SARS-CoV-2 was believed to be the trigger for the thyroid storm, considering that the patient tested positive for COVID-19 two days before the seizures. CONCLUSION: In pregnant women presenting with seizures or changes in consciousness, the possibility of a thyroid storm should be considered. There are various causes for a thyroid storm, but given the recent pandemic, it is necessary to bear in mind that the thyroid storm may be precipitated by COVID-19.

Photoelectrochemical Nitrate Reduction to Ammonia on Ordered Silicon Nanowire Array Photocathodes.

As a new path to "green" ammonia production, photoelectrochemical nitrate reduction reaction (PEC NO3 RR) is investigated for the first time. An Au-decorated ordered silicon nanowire (O_SiNW) array photocathode demonstrates 95.6 % of Faradaic efficiency (FE) to ammonia at 0.2 VRHE , which represents a more positive potential than the thermodynamic reduction potential of nitrate by utilizing photovoltage. The high FE is possible because both Si and Au surfaces are inactive for competing water reduction to hydrogen. The O_SiNW array structure is favorable to promote the PEC NO3 RR relative to planar Si or randomly-grown Si nanowire, by enabling the uniform distribution of small Au nanoparticles as an electrocatalyst and facilitating the mass transport during the reaction. The results demonstrate the feasibility of PEC nitrate conversion to ammonia and would motivate further studies and developments.

Microbiota of Breast Tissue and Its Potential Association with Regional Recurrence of Breast Cancer in Korean Women.

Recent studies have reported dysbiosis of the microbiome in breast tissue collected from patients with breast cancer and the association between the microbiota and disease progression. However, the role of the microbiota in breast tissue remains unclear, possibly due to the complexity of breast cancer and various factors, including racial and geographical differences, influencing microbiota in breast tissue. Here, to determine the potential role of microbiota in breast tumor tissue, we analyzed 141 tissue samples based on three different tissue types (tumor, adjacent normal, and lymph node tissues) from the same patients with breast cancer in Korea. The microbiota was not simply distinguishable based on tissue types. However, the microbiota could be divided into two cluster types, even within the same tissue type, and the clinicopathologic factors were differently correlated in the two cluster types. Risk of regional recurrence was also significantly different between the microbiota cluster types (p = 0.014). In predicted function analysis, the pentose and glucuronate interconversions were significantly different between the cluster types (q < 0.001), and Enterococcus was the main genus contributing to these differences (q < 0.01). Results showed that the microbiota of breast tissue could interact with the host and influence the risk of regional recurrence. Although further studies would be recommended to validate our results, this study could expand our understanding on the breast tissue microbiota, and the results might be applied to develop novel prediction methods and treatments for patients with breast cancer.

Trend in Prevalence of Smoking and Motivation to Quit among Korean Adult Male Cancer Survivors over the Last 8 Years: The Korea National Health and Nutrition Examination Survey V-VII (2010-2017).

BACKGROUND: This study aimed to investigate trends in the prevalence of current smokers and motivation to quit among Korean male cancer survivors. METHODS: Out of 20,012 men who participated in the Korea National Health and Nutrition Examination Survey V (2010-2012), VI (2013-2015), and VII (2016-2017), 742 cancer survivors were included. A cancer survivor was defined as a person who concurred to the item, "The cancer has been diagnosed by a doctor" in the health questionnaire. Smoking status was classified as current, former, and never smokers. Regarding motivation to quit smoking, we defined those who had a willingness to quit within 6 months as the willing group. Logistic regression analysis was conducted to examine trends in the prevalence of current smokers and the proportion of the willing group among current smokers. RESULTS: Overall, 3.7% of Korean men who participated in the study were cancer survivors. Current smokers constituted 19.5%, 19.1%, and 15.3% of cancer survivors in phases V, VI, and VII respectively which did not show significant changes (P for trend=0.33). However, the proportion of current smokers in the non-cancer group was significantly reduced to 46.6%, 41.2%, and 38.9% in phases V, VI, and VII, respectively (P for trend <0.001). The proportion of those with a motivation to quit smoking did not show a significant trend in the cancer survivors (P for trend=0.964) and non-cancer group (P for trend=0.884). CONCLUSION: Prevalence of current smokers and motivation to quit in Korean male cancer survivors did not show significant trends.

A Graphene-Based Enzymatic Biosensor Using a Common-Gate Field-Effect Transistor for L-Lactic Acid Detection in Blood Plasma Samples.

Lactate is an important organic molecule that is produced in excess during anaerobic metabolism when oxygen is absent in the human organism. The concentration of this substance in the body can be related to several medical conditions, such as hemorrhage, respiratory failure, and ischemia. Herein, we describe a graphene-based lactate biosensor to detect the concentrations of L-lactic acid in different fluids (buffer solution and plasma). The active surface (graphene) of the device was functionalized with lactate dehydrogenase enzyme using different substances (Nafion, chitosan, and glutaraldehyde) to guarantee stability and increase selectivity. The devices presented linear responses for the concentration ranges tested in the different fluids. An interference study was performed using ascorbic acid, uric acid, and glucose, and there was a minimum variation in the Dirac point voltage during detection of lactate in any of the samples. The stability of the devices was verified at up to 50 days while kept in a dry box at room temperature, and device operation was stable until 12 days. This study demonstrated graphene performance to monitor L-lactic acid production in human samples, indicating that this material can be implemented in more simple and low-cost devices, such as flexible sensors, for point-of-care applications.

Impact of image compression on deep learning-based mammogram classification.

Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms as "malignant"-cases that lead to a cancer diagnosis and treatment-or "normal" and "benign," non-malignant cases that do not require immediate medical intervention. In this retrospective study, 9111 unique mammograms-5672 normal, 1686 benign, and 1754 malignant cases were collected from the National Cancer Center in the Republic of Korea. Image compression was applied to mammograms with compression ratios (CRs) ranging from 15 to 11 K. Convolutional neural networks (CNNs) with three convolutional layers and three fully-connected layers were trained using these images to classify a mammogram as malignant or not malignant across a range of CRs using five-fold cross-validation. Models trained on images with maximum CRs of 5 K had an average area under the receiver operating characteristic curve (AUROC) of 0.87 and area under the precision-recall curve (AUPRC) of 0.75 across the five folds and compression ratios. For images compressed with CRs of 10 K and 11 K, model performance decreased (average 0.79 in AUROC and 0.49 in AUPRC). Upon generating saliency maps that visualize the areas each model views as significant for prediction, models trained on less compressed (CR < = 5 K) images had maps encapsulating a radiologist's label, while models trained on images with higher amounts of compression had maps that missed the ground truth completely. In addition, base ResNet18 models pre-trained on ImageNet and trained using compressed mammograms did not show performance improvements over our CNN model, with AUROC and AUPRC values ranging from 0.77 to 0.87 and 0.52 to 0.71 respectively when trained and tested on images with maximum CRs of 5 K. This paper finds that while training models on images with increased the robustness of the models when tested on compressed data, moderate image compression did not substantially impact the classification performance of DL-based models.

Sensitive electrochemical biosensor combined with isothermal amplification for point-of-care COVID-19 tests.

We report an electrochemical biosensor combined with recombinase polymerase amplification (RPA) for rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2. The electrochemical biosensor based on a multi-microelectrode array allows the detection of multiple target genes by differential pulse voltammetry. The RPA reaction involves hybridization of the RPA amplicon with thiol-modified primers immobilized on the working electrodes, which leads to a reduction of current density as amplicons accumulate. The assay results in shorter "sample-to-answer" times than conventional PCR without expensive thermo-cycling equipment. The limits of detection are about 0.972 fg/µL (RdRP gene) and 3.925 fg/µL (N gene), which are slightly lower than or comparable to that of RPA assay results obtained by gel electrophoresis without post-amplification purification. The combination of electrochemical biosensors and the RPA assay is a rapid, sensitive, and convenient platform that can be potentially used as a point-of-care test for the diagnosis of COVID-19.

Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.

BACKGROUND: Mammography is the current standard for breast cancer screening. This study aimed to develop an artificial intelligence (AI) algorithm for diagnosis of breast cancer in mammography, and explore whether it could benefit radiologists by improving accuracy of diagnosis. METHODS: In this retrospective study, an AI algorithm was developed and validated with 170 230 mammography examinations collected from five institutions in South Korea, the USA, and the UK, including 36 468 cancer positive confirmed by biopsy, 59 544 benign confirmed by biopsy (8827 mammograms) or follow-up imaging (50 717 mammograms), and 74 218 normal. For the multicentre, observer-blinded, reader study, 320 mammograms (160 cancer positive, 64 benign, 96 normal) were independently obtained from two institutions. 14 radiologists participated as readers and assessed each mammogram in terms of likelihood of malignancy (LOM), location of malignancy, and necessity to recall the patient, first without and then with assistance of the AI algorithm. The performance of AI and radiologists was evaluated in terms of LOM-based area under the receiver operating characteristic curve (AUROC) and recall-based sensitivity and specificity. FINDINGS: The AI standalone performance was AUROC 0·959 (95% CI 0·952-0·966) overall, and 0·970 (0·963-0·978) in the South Korea dataset, 0·953 (0·938-0·968) in the USA dataset, and 0·938 (0·918-0·958) in the UK dataset. In the reader study, the performance level of AI was 0·940 (0·915-0·965), significantly higher than that of the radiologists without AI assistance (0·810, 95% CI 0·770-0·850; p<0·0001). With the assistance of AI, radiologists' performance was improved to 0·881 (0·850-0·911; p<0·0001). AI was more sensitive to detect cancers with mass (53 [90%] vs 46 [78%] of 59 cancers detected; p=0·044) or distortion or asymmetry (18 [90%] vs ten [50%] of 20 cancers detected; p=0·023) than radiologists. AI was better in detection of T1 cancers (73 [91%] vs 59 [74%] of 80; p=0·0039) or node-negative cancers (104 [87%] vs 88 [74%] of 119; p=0·0025) than radiologists. INTERPRETATION: The AI algorithm developed with large-scale mammography data showed better diagnostic performance in breast cancer detection compared with radiologists. The significant improvement in radiologists' performance when aided by AI supports application of AI to mammograms as a diagnostic support tool. FUNDING: Lunit.

Effect of the similarity of gut microbiota composition between donor and recipient on graft function after living donor kidney transplantation.

Graft outcomes of unrelated donor kidney transplant are comparable with those of related donor kidney transplant despite their genetic distance. This study aimed to identify whether the similarity of donor-recipient gut microbiota composition affects early transplant outcomes. Stool samples from 67 pairs of kidney transplant recipients and donors were collected. Gut microbiota differences between donors and recipients were determined using weighted UniFrac distance. Among the donor-recipient pairs, 30 (44.8%) pairs were related, while 37 (55.2%) were unrelated. The unrelated pairs, especially spousal pairs, had similar microbial composition, and they more frequently shared their meals than related pairs did. The weighted UniFrac distance showed an inverse correlation with the 6-month allograft function (p = 0.034); the correlation was significant in the unrelated pairs (p = 0.003). In the unrelated pairs, the microbial distance showed an excellent accuracy in predicting the estimated glomerular filtration rate of < 60 mL/min/1.73 m2 at 6-months post-transplantation and was better than human leukocyte antigen incompatibility and rejection. The incidence of infection within 6 months post-transplantation increased in the recipients having dissimilar microbiota with donors compared to the other recipients. Thus, pre-transplantation microbial similarity in unrelated donors and recipients may be associated with 6-month allograft function.

Changes in Bond Strength and Topography for Y-TZP Etched with Hydrofluoric Acid Depending on Concentration and Temperature Conditions.

Background and objectives: This study aimed to investigate the change in bond strength between resin cement and tetragonal zirconia polycrystalline stabilized with 3 to 8 mol% yttrium oxide (Y-TZP) and observe the topographical change of the Y-TZP surface when etched with hydrofluoric acid (HF) solution under different concentration and temperature conditions. Materials and Methods: Non-etched sintered Y-TZP specimens under two different temperature conditions (room temperature and 70-80 °C, respectively), were used as a control, while experimental groups were etched with 5%, 10%, 20%, and 40% HF solutions for 10 min. After zirconia primer and MDP-containing resin cement were applied to the Y-TZP surface, the shear bond strength (SBS) of each experimental group was measured. Results: Under room temperature conditions, the highest SBS value was measured in the 40% HF etching group, representing a significant deviation from the other groups (p < 0.05). In the 70-80 °C tests, the 40% HF etching group also had the highest SBS value, but there was no significant difference when compared to the 20% HF etching group (p > 0.05). From SEM and AFM observations, the HF solution increasingly dissolved the Y-TZP surface grain structure as the concentration and application temperature rose, resulting in high surface roughness and irregularities. Conclusions: Pretreating with either 20% HF solution at 70-80 °C or 40% HF solution at room temperature and 70-80 °C effectively acid etched the Y-TZP surface, resulting in more surface roughness and irregularities. Accounting for the concentration and temperature conditions of the HF solution, using 40% HF solution at room temperature will result in improvements in adhesion between resin cement and Y-TZP.

Granulomatous peritonitis caused by iatrogenic spillage of ovarian dermoid cystectomy: a case report and literature review.

A 39-year-old nulliparous woman experienced continuous mild fever and abdominal pain since undergoing laparoscopic ovarian dermoid cystectomy 3 months previously in a local hospital. Abdominal computed tomography revealed diffuse heterogeneous fat infiltrations with numerous micronodules in the greater and lesser omentum, combined with ascites with thickening of the parietal peritoneum. The patient underwent exploratory laparoscopy, which included partial pelvic peritonectomy, excision of granulomas, and adhesiolysis with massive irrigation. The patient was treated successfully with laparoscopic surgery and all reproductive structures were spared without operative complications. To avoid peritonitis, complete removal of cyst contents and massive irrigation should be performed during ovarian dermoid cystectomy. Conservative surgical treatment may be a good choice for treating granulomatous peritonitis induced by iatrogenic rupture.

The Association between Gut Microbiota and Uremia of Chronic Kidney Disease.

Chronic kidney disease (CKD)-associated uremia aggravates-and is aggravated by-gut dysbiosis. However, the correlation between CKD severity and gut microbiota and/or their uremic metabolites is unclear. We enrolled 103 CKD patients with stage 1 to 5 and 46 healthy controls. We analyzed patients' gut microbiota by MiSeq system and measured the serum concentrations of four uremic metabolites (p-cresyl sulfate, indoxyl sulfate, p-cresyl glucuronide, and trimethylamine N-oxide) by liquid chromatography-tandem mass spectrometry. Serum concentrations of the uremic metabolites increased with kidney function deterioration. Gut microbial diversity did not differ among the examined patient and control groups. In moderate or higher stage CKD groups, Oscillibacter showed positive interactions with other microbiota, and the proportions of Oscillibacter were positively correlated with those of the uremic metabolites. The gut microbiota, particularly Oscillibacter, was predicted to contribute to pyruvate metabolism which increased with CKD progression. Relative abundance of Oscillibacter was significantly associated with both serum uremic metabolite levels and kidney function. Predicted functional analysis suggested that kidney-function-associated changes in the contribution of Oscillibacter to pyruvate metabolism in CKD may greatly affect the gut environment according to kidney function, resulting in dysbiosis concomitant with uremic toxin production. The gut microbiota could be associated with uremia progression in CKD. These results may provide basis for further metagenomics analysis of kidney diseases.

Monitoring the hemostasis process through the electrical characteristics of a graphene-based field-effect transistor.

Graphene-based transistors are promising devices in the evaluation of carrier density in biological analytes. We report on the design and fabrication of a graphene-based field-effect transistor for monitoring and assessing the interaction between the coagulation factors based on the charge carrier density in a blood sample. When biochemical reactions occurred during the coagulation cascade process, a dopant effect was noticed on the graphene surface by the change in Dirac point voltage values. Additional experiments were performed using blood samples treated with activators (vitamin K, calcium chloride, and thromboplastin reagent) and inhibitors (heparin drugs) to evaluate the selectivity of the graphene field-effect transistor devices. Since the transfer characteristic curves presented divergent behaviours for different levels of procoagulants and anticoagulants, the measurements showed that the devices can assess changes in the concentrations of factors that inhibit or accelerate the cascade process when using untreated and treated samples. Reproducibility was verified by testing samples from different sources. To the best of our knowledge, this study is the first to demonstrate the potential of graphene in monitoring the hemostasis process through the analysis of the electrical properties of human whole blood.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.