Extraction and Characterization of Human Adipose Tissue-Derived Collagen: Toward Xeno-Free Tissue Engineering.

BACKGROUND: Collagen is a key component of connective tissue and has been frequently used in the fabrication of medical devices for tissue regeneration. Human-originated collagen is particularly appealing due to its low immune response as an allograft biomaterial compared to xenografts and its ability to accelerate the regeneration process. Ethically and economically, adipose tissues available from liposuction clinics are a good resource to obtain human collagen. However, studies are still scarce on the extraction and characterization of human collagen, which originates from adipose tissue. The aim of this study is to establish a novel and simple method to extract collagen from human adipose tissue, characterize the collagen, and compare it with commercial-grade porcine collagen for tissue engineering applications. METHODS: We developed a method to extract the collagen from human adipose tissue under quasi-Good Manufacturing Practice (GMP) conditions, including freezing the tissue, blood removal, and ethanol-based purification. Various techniques, including protein quantification, decellularization assessment, SDS-PAGE, FTIR, and CD spectroscopy analysis, were used for characterization. Amino acid composition was compared with commercial collagen. Biocompatibility and cell proliferation tests were performed, and in vitro tests using collagen sponge scaffolds were conducted with statistical analysis. RESULTS: Our results showed that this human adipose-derived collagen was equivalent in quality to commercially available porcine collagen. In vitro testing demonstrated high cell attachment and the promotion of cell proliferation. CONCLUSION: In conclusion, we developed a simple and novel method to extract and characterize collagen and extracellular matrix from human adipose tissue, offering a potential alternative to animal-derived collagen for xeno-free tissue engineering applications.

Resident Impact of the Single Site Order Restricting Staff Mobility across Long-Term Care Homes in British Columbia, Canada.

The Single Site Order (SSO)-a policy restricting staff from working at multiple long-term care (LTC) homes-was mandated by the Public Health Agency of Canada to control the spread of COVID-19 in LTC homes, where nearly 70% of COVID-19-related deaths in Canada occurred. This mixed methods study assesses the impact of the SSO on LTC residents in British Columbia. Interviews were conducted (residents (n = 6), family members (n = 9), staff (n = 18), and leadership (n = 10) from long-term care homes (n = 4)) and analyzed using thematic analysis. Administrative data were collected between April 2019 and March 2020 and between April 2020 and March 2021 and analyzed using descriptive statistics and data visualization. Qualitative and quantitative data were triangulated and demonstrated that staffing challenges became worse during the implementation of the SSO, resulting in the mental and physical health deterioration of LTC residents. Qualitative data demonstrated decreased time for personalized and proactive care, increased communication challenges, and increased loneliness and isolation. Quantitative data showed a decline in activities of daily living, increased antipsychotic medication use, pressure ulcers, behavioural symptoms, and an increase in falls. Addressing staff workload and staffing shortages during SSO-related policy implementation is essential to avoid resident health deterioration.

Immune-checkpoint proteins, cytokines, and microbiome impact on patients with cervical insufficiency and preterm birth.

Background: Microenvironmental factors, including microbe-induced inflammation and immune-checkpoint proteins that modulate immune cells have been associated with both cervical insufficiency and preterm delivery. These factors are incompletely understood. This study aimed to explore and compare interactions among microbiome and inflammatory factors, such as cytokines and immune-checkpoint proteins, in patients with cervical insufficiency and preterm birth. In particular, factors related to predicting preterm birth were identified and the performance of the combination of these factors was evaluated. Methods: A total of 220 swab samples from 110 pregnant women, prospectively recruited at the High-Risk Maternal Neonatal Intensive Care Center, were collected between February 2020 and March 2021. This study included 63 patients with cervical insufficiency receiving cerclage and 47 control participants. Endo- and exocervical swabs and fluids were collected simultaneously. Shotgun metagenomic sequencing for the microbiome and the measurement of 34 immune-checkpoint proteins and inflammatory cytokines were performed. Results: First, we demonstrated that immune-checkpoint proteins, the key immune-regulatory molecules, could be measured in endocervical and exocervical samples. Secondly, we identified significantly different microenvironments in cervical insufficiency and preterm birth, with precise cervical locations, to provide information about practically useful cervical locations in clinical settings. Finally, the presence of Moraxella osloensis (odds ratio = 14.785; P = 0.037) and chemokine CC motif ligand 2 levels higher than 73 pg/mL (odds ratio = 40.049; P = 0.005) in endocervical samples were associated with preterm birth. Combining M. osloensis and chemokine CC motif ligand 2 yielded excellent performance for predicting preterm birth (area under the receiver operating characteristic curve = 0.846, 95% confidence interval = 0.733-0.925). Conclusion: Multiple relationships between microbiomes, immune-checkpoint proteins, and inflammatory cytokines in the cervical microenvironment were identified. We focus on these factors to aid in the comprehensive understanding and therapeutic modulation of local microbial and immunologic compositions for the management of cervical insufficiency and preterm birth.

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort. PURPOSE: To develop a deep learning model for 3D breast cancer segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using weak annotation with reliable performance. STUDY TYPE: Retrospective. POPULATION: Seven hundred and thirty-six women with breast cancer from a single institution, divided into the development (N = 544) and test dataset (N = 192). FIELD STRENGTH/SEQUENCE: 3.0-T, 3D fat-saturated gradient-echo axial T1-weighted flash 3D volumetric interpolated brain examination (VIBE) sequences. ASSESSMENT: Two radiologists performed a weak annotation of the ground truth using bounding boxes. Based on this, the ground truth annotation was completed through autonomic and manual correction. The deep learning model using 3D U-Net transformer (UNETR) was trained with this annotated dataset. The segmentation results of the test set were analyzed by quantitative and qualitative methods, and the regions were divided into whole breast and region of interest (ROI) within the bounding box. STATISTICAL TESTS: As a quantitative method, we used the Dice similarity coefficient to evaluate the segmentation result. The volume correlation with the ground truth was evaluated with the Spearman correlation coefficient. Qualitatively, three readers independently evaluated the visual score in four scales. A P-value <0.05 was considered statistically significant. RESULTS: The deep learning model we developed achieved a median Dice similarity score of 0.75 and 0.89 for the whole breast and ROI, respectively. The volume correlation coefficient with respect to the ground truth volume was 0.82 and 0.86 for the whole breast and ROI, respectively. The mean visual score, as evaluated by three readers, was 3.4. DATA CONCLUSION: The proposed deep learning model with weak annotation may show good performance for 3D segmentations of breast cancer using DCE-MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Factors affecting unmet healthcare needs in female baby boomers: Andersen model application in Korea.

This study aimed to measure unmet healthcare needs and investigate the factors affecting them in female baby boomers (individuals born between 1955 and 1963) using the Korea Health Panel Data 2017 from February to June 2017 by the Korea Institute for Health and Social Affairs and the National Health Insurance Corporation. The data were analyzed using descriptive statistics, chi-square test, t-test, and multiple logistic regression using SPSS WIN 25.0 program. The results showed that the proportion of unmet healthcare needs was 11.1%, and the primary reason for unmet healthcare needs was the lack of visitation time. Female baby boomers experienced more unmet healthcare needs when they had no spouse (1.63 times), eating problems (2.33 times), and stress (1.31 times). This study is significant because it measured the unmet healthcare needs of women in the baby boomer generation and identified the factors influencing unmet healthcare needs. The study's results can help provide essential data to decrease the unmet healthcare needs of female baby boomers.

Impact of the single site order in LTC: exacerbation of an overburdened system.

BACKGROUND: The long-term care (LTC) sector has been at the epicentre of COVID-19 in Canada. This study aimed to understand the impact that the Single Site Order (SSO) had on staff and leadership in four LTC homes in the Lower Mainland of British Columbia, Canada. METHODS: A mixed method study was conducted by analyzing administrative staffing data. Overtime, turnover, and job vacancy data were extracted and analyzed from four quarters before (April 2019 - March 2020) and four quarters during the pandemic (April 2020 - March 2021) using scatterplots and two-part linear trendlines across total direct care nursing staff and by designation (i.e., registered nurses (RNs), licenced practical nurses (LPNs) and care aids (CAs)). Virtual interviews were conducted with a purposive sample of leadership (10) and staff (18) from each of the four partner care homes (n = 28). Transcripts were analyzed in NVivo 12 using thematic analysis. RESULTS: Quantitative data indicated that the total overtime rate increased from before to during the pandemic, with RNs demonstrating the steepest rate increase. Additionally, while rates of voluntary turnover showed an upward trend before the pandemic for all direct care nursing staff, the rate for LPNs and, most drastically, for RNs was higher during the pandemic, while this rate decreased for CAs. Qualitative analysis identified two main themes and sub-themes: (1) overtime (loss of staff, mental health, and sick leave) and (2) staff turnover (the need to train new staff, and gender/race) as the most notable impacts associated with the SSO. CONCLUSIONS: The results of this study indicate that the outcomes due to COVID-19 and the SSO are not equal across nursing designations, with the RN shortage in the LTC sector highly evident. Quantitative and qualitative data underscore the substantial impact the pandemic and associated policies have on the LTC sector, namely, that staff are over-worked and care homes are understaffed.

A Natural Virucidal and Microbicidal Spray Based on Polyphenol-Iron Sols.

Numerous disinfection methods have been developed to reduce the transmission of infectious diseases that threaten human health. However, it still remains elusively challenging to develop eco-friendly and cost-effective methods that deactivate a wide range of pathogens, from viruses to bacteria and fungi, without doing any harm to humans or the environment. Herein we report a natural spraying protocol, based on a water-dispersible supramolecular sol of nature-derived tannic acid (TA) and Fe3+, which is easy-to-use and low-cost. Our formulation effectively deactivates viruses (influenza A viruses, SARS-CoV-2, and human rhinovirus) as well as suppressing the growth and spread of pathogenic bacteria (Escherichia coli, Salmonella typhimurium, Staphylococcus aureus, and Acinetobacter baumannii) and fungi (Pleurotus ostreatus and Trichophyton rubrum). Its versatile applicability in a real-life setting is also demonstrated against microorganisms present on the surfaces of common household items (e.g., air filter membranes, disposable face masks, kitchen sinks, mobile phones, refrigerators, and toilet seats).

Primary Ovarian Neuroendocrine Carcinoid Tumor Arising in a Mature Cystic Teratoma.

Primary ovarian neuroendocrine carcinoid tumors are extremely rare. However, their clinical course is good, and hence, fertility-sparing surgery is a feasible treatment option in cases of unilateral localized lesions. In this report, we present the case of a 20-year-old nulliparous woman who was diagnosed as having a primary ovarian neuroendocrine carcinoid tumor arising from a mature cystic teratoma. She underwent laparoscopic right ovarian cystectomy, and her postoperative recovery was uneventful. The patient has been under close observation over a 1-year follow-up period and has shown no evidence of tumor recurrence.

SB8, an approved bevacizumab biosimilar based on totality of evidence: scientific justification of extrapolation.

SB8 is a biosimilar of bevacizumab based on its similarity demonstrated by physicochemical, functional, non-clinical and clinical studies. Supported by the concept of extrapolation, SB8 was authorized and is used in a similar manner across all types of tumors as reference bevacizumab. Furthermore, SB8 offers convenience with prolonged stability compared with reference bevacizumab in diluted form. Although a biosimilar must demonstrate biosimilarity to a reference product with the 'totality of evidence' in a stringent regulatory process for marketing authorization, some concerns remain among healthcare practitioners, particularly about extrapolation. This review summarizes the concepts of the totality of evidence and extrapolation in biosimilar development and the role of bevacizumab biosimilars in the management of metastatic colorectal cancer as an extrapolated indication.

Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are a group of hematologic neoplasms accompanied by dysplasia of the bone marrow hematopoietic cells with cytopenia. Detecting dysplasia is important in the diagnosis of MDS, but it takes considerable time and effort. Also, since the assessment of dysplasia is subjective and difficult to quantify, a more efficient tool is needed for quality control and standardization of bone marrow aspiration smear interpretation. In this study, we developed and evaluated an algorithm to automatically discriminate hematopoietic cell lineages and detect dysplastic cells in bone marrow aspiration smears using deep learning technology. Bone marrow aspiration images were acquired from 34 patients diagnosed with MDS and from 24 normal bone marrow slides. In total, 8065 cells were classified into eight categories: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, blasts, and others. The algorithm demonstrated acceptable performance in classifying dysplastic cells, with an AUC of 0.945-0.996 and accuracy of 0.912-0.993. The algorithm developed in this study could be used as an auxiliary tool for diagnosing patients with MDS and is expected to contribute to shortening the time required for MDS bone marrow aspiration diagnosis and standardizing visual reading.

Prognostic impacts of soluble immune checkpoint regulators and cytokines in patients with SARS-CoV-2 infection.

Coronavirus disease 2019 (COVID-19) has been a pandemic for the past two years. Predicting patient prognosis is critical. Although immune checkpoints (ICs) were shown to be involved in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, quantitative studies of ICs in clinical practice are limited. In this study, various soluble ICs (sICs) and cytokine levels in patients with SARS-CoV-2 infection at different time points were compared between survivors and deaths; we also examined whether sICs are useful for predicting prognosis. sICs and cytokines were measured in serum samples from 38 patients diagnosed with COVID-19 in the first and second week post-diagnosis. All assays were performed by bead-based multiplexed immunoassay system using Luminex Bio-Plex 200 system. The correlation of sICs and cytokines with laboratory markers was evaluated, and the levels of sICs in survivors were compared with those in deaths. Among the sICs, the second-week levels of soluble cluster of differentiation (sCD27, p = 0.012), sCD40 (p< 0.001), cytotoxic T-lymphocyte-associated protein 4 (sCTLA-4, p< 0.001), herpes virus entry mediator (sHVEM, p = 0.026), and T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3, p = 0.002) were significantly higher in deaths than in survivors. The levels of nine cytokines assessed in the second week of deaths were significantly higher than those in survivors. The sICs sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines chemokine CC motif ligand 2 (CCL2), GM-CSF, IL-10, and IL-8 showed significant positive correlations with the levels of C-reactive protein (CRP) and procalcitonin and were negatively correlated with the absolute lymphocyte count and platelet values. Increased levels of sICs including sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines were significant factors for poor prognosis. sICs, together with cytokines and inflammatory markers, may be useful as prognostic stratification markers in SARS-CoV-2-infected patients.

STANDARD M10 SARS-CoV-2 Assay for Rapid Detection of SARS-CoV-2: Comparison of Four Real-Time PCR Assays.

The demand for assays that can rapidly and accurately detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains high. We evaluated the performance of two rapid real-time reverse transcription polymerase chain reaction (RT-qPCR) assays (STANDARD M10 SARS-CoV-2 and Xpert Xpress SARS-CoV-2) against conventional RT-qPCR assays (STANDARD M nCoV and Allplex SARS-CoV-2) for detecting SARS-CoV-2. A total of 225 swab samples were collected and tested using the four assays. The STANDARD M10 SARS-CoV-2 assay showed 97.4% positive percent agreement (PPA) and 100.0% negative percent agreement (NPA) compared to the STANDARD M nCoV assay and Allplex SARS-CoV-2 assay. STANDARD M10 exhibited high performance except in samples with low viral loads (cycle threshold (Ct) > 30). Xpert Xpress showed PPA and NPA of 100.0% compared to the two conventional RT-qPCR assays. The kappa coefficient (Κ) showed nearly almost perfect agreement between each assay and conventional RT-qPCR assays. The correlations of Ct values between the two rapid RT-qPCR and conventional RT-qPCR assays were >0.8, indicating strong correlations. All included assays could detect SARS-CoV-2 variants, such as the Alpha, Beta, and Gamma variants. The recently developed STANDARD M10 has a shorter turnaround time and random-access detection on automated devices, thereby facilitating efficient testing in emergency settings.

Development and validation of a deep learning-based protein electrophoresis classification algorithm.

BACKGROUND: Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability. METHODS: A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks. RESULTS: Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%. CONCLUSIONS: The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.

Quantitative Analysis of Anti-N and Anti-S Antibody Titers of SARS-CoV-2 Infection after the Third Dose of COVID-19 Vaccination.

We quantitatively analyzed SARS-CoV-2 antibody levels in patients after two doses of the ChAdOx1 nCoV-19 vaccine and the third BNT162b2 booster. We obtained 255 serum samples from 149 healthcare workers 1 and 4 months after the third dose. Of the 149 participants, 58 (38.9%) experienced COVID-19 infection during the 4-month study period, with infection occurring 7−62 days before the second blood draw. Total antibody titers against the anti-spike (anti-S) and anti-nucleocapsid (anti-N) proteins of SARS-CoV-2 were measured using Elecsys Anti-SARS-CoV-2 S and Elecsys Anti-SARS-CoV-2 assays (Roche), respectively. The median anti-S antibody titer in the non-infected groups at 4 months after the third dose was significantly decreased compared to that at 1 month after the third dose (from 17,777 to 3673 U/mL, p < 0.001). The infected group showed higher median anti-S antibody titers at 4 months (19,539 U/mL) than the non-infected group (3673 U/mL). The median anti-N antibody titer in the infected group at 4 months after the third dose was a 5.07 cut-off index (79.3% positivity). Anti-N antibody titers in the infected group were correlated with the number of days after SARS-CoV-2 infection. These data provide useful information for determining quarantine strategies and fourth vaccination requirements.

Humoral and Cellular Responses to BNT162b2 as a Booster Following Two Doses of ChAdOx1 nCov-19 Determined Using Three SARS-CoV-2 Antibody Assays and an Interferon-Gamma Release Assay: A Prospective Longitudinal Study in Healthcare Workers.

Data on humoral and cellular responses to BNT162b2 as a booster dose, following two doses of ChAdOx1 nCov-19 vaccine, have seldom been reported. The aim of this study was to assess the positivity rates of three representative antibody assays targeting total, IgG, and neutralizing antibodies, and an interferon-γ release assay (IGRA), and to determine the longitudinal changes in quantitative antibody titers after each vaccination. A total of 1027 samples were collected from healthcare workers. The number of participants after the booster dose was 153, and they all completed a questionnaire on adverse reactions. All antibody assays showed 100.0% positivity at 1 month after booster vaccination. The median antibody titers of the assays were significantly increased compared with those after the second dose (22.1-fold increase for Roche total antibody, 14.0-fold increase for Abbott IgG, and 1.1-fold increase (97.5% inhibition) for GenScript neutralizing antibody). Cellular responses determined using the IGRA were positive in 92.8% of the participants. Most participants (72.5%) reported mild adverse reactions. Correlations between the three antibody assays and IGRA were weak or negligible, indicating a difference between humoral and cellular responses. Overall, our study provides information about booster vaccine strategies and laboratory settings, which could subsequently contribute to the control of the spread of coronavirus disease 2019.

Effects of Hexagonal Boron Nitride Insulating Layers on the Driving Performance of Ionic Electroactive Polymer Actuators for Light-Weight Artificial Muscles.

To improve the energy efficiency and driving performance of ionic electroactive polymer actuators, we propose inserting insulating layers of 170 nm hexagonal boron nitride (h-BN) particles between the ionic polymer membrane and electrodes. In experiments, actuators exhibited better capacitance (4.020 × 10-1 F), displacement (6.01 mm), and curvature (35.59 m-1) with such layers than without them. The excellent insulating properties and uniform morphology of the layers reduced the interfacial resistance, and the ion conductivity (0.071 S m-1) within the ionic polymer improved significantly. Durability was enhanced because the h-BN layer is chemically and thermally stable and efficiently blocks heat diffusion and ion hydrate evaporation during operation. The results demonstrate a close relationship between the capacitance and driving performance of actuators. A gripper prepared from the proposed ionic electroactive polymer actuator can stably hold an object even under strong external vibration and fast or slow movement.

Impact of COVID-19 on Antimicrobial Consumption and Spread of Multidrug-Resistance in Bacterial Infections.

The spread of COVID-19 pandemic may have affected antibiotic consumption patterns and the prevalence of colonized or infected by multidrug-resistant (MDR) bacteria. We investigated the differences in the consumption of antibiotics easily prone to resistance and the prevalence of MDR bacteria during the COVID-19 pandemic (March 2020 to September 2021) compared to in the pre-pandemic period (March 2018 to September 2019). Data on usage of antibiotics and infections caused by methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Enterobacteriaceae (CRE), carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were obtained from hospitalized patients in four university hospitals. The consumption of penicillin with ß-lactamase inhibitors (3.4% in ward, 5.8% in intensive care unit (ICU)), and carbapenems (25.9% in ward, 12.1% in ICU) increased during the pandemic period. The prevalence of MRSA (4.7%), VRE (49.0%), CRE (22.4%), and CRPA (20.1%) isolated in clinical samples from the ward and VRE (26.7%) and CRE (36.4%) isolated in clinical samples from the ICU were significantly increased, respectively. Meanwhile, only the prevalence of CRE (38.7%) isolated in surveillance samples from the ward increased. The COVID-19 pandemic is associated with increased consumption of antibiotics and has influenced the prevalence of infections caused by MDR isolates.

Prevalence and Clinical Impact of Coinfection in Patients with Coronavirus Disease 2019 in Korea.

Coinfection rates with other pathogens in coronavirus disease 2019 (COVID-19) varied during the pandemic. We assessed the latest prevalence of coinfection with viruses, bacteria, and fungi in COVID-19 patients for more than one year and its impact on mortality. A total of 436 samples were collected between August 2020 and October 2021. Multiplex real-time PCR, culture, and antimicrobial susceptibility testing were performed to detect pathogens. The coinfection rate of respiratory viruses in COVID-19 patients was 1.4%. Meanwhile, the rates of bacteria and fungi were 52.6% and 10.5% in hospitalized COVID-19 patients, respectively. Respiratory syncytial virus, rhinovirus, Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans were the most commonly detected pathogens. Ninety percent of isolated A. baumannii was non-susceptible to carbapenem. Based on a multivariate analysis, coinfection (odds ratio [OR] = 6.095), older age (OR = 1.089), and elevated lactate dehydrogenase (OR = 1.006) were risk factors for mortality as a critical outcome. In particular, coinfection with bacteria (OR = 11.250), resistant pathogens (OR = 11.667), and infection with multiple pathogens (OR = 10.667) were significantly related to death. Screening and monitoring of coinfection in COVID-19 patients, especially for hospitalized patients during the pandemic, are beneficial for better management and survival.

Place-of-death preferences among patients with cancer and family caregivers in inpatient and outpatient palliative care.

OBJECTIVE: Meeting the preferences of patients is considered an important palliative care outcome. Prior studies reported that more than 80% of patients with terminally ill cancer prefer to die at home. The purpose of this study was to determine place-of-death preference among palliative care patients in the outpatient centre and the palliative care unit (PCU) of a comprehensive cancer centre. METHODS: A cross-sectional anonymous questionnaire was administered to patients with advanced cancer and caregivers (PCU and outpatient centre) between August 2012 and September 2014. PCU patients responded when there was no delirium and the primary caregiver responded when the patient was unable to respond. In the case of outpatients, dyads were assessed. The survey was repeated 1 month later. RESULTS: Overall, 65% preferred home death. There was less preference for home death among PCU patients (58%) than among outpatients (72%). Patient and caregiver agreement regarding preferred place of death for home was 86%. After 1 month, outpatients were significantly more likely than PCU patients to have the same preferred place of death as they had 1 month earlier (96% vs 83%; p=0.003). CONCLUSIONS: Although home was the preferred place of death in our group of patients with advanced cancer and their caregivers, a substantial minority preferred hospital death or had no preference. We speculate that PCU patients' higher preference for hospital death is likely related to more severe distress because they had already tried home care. Personalised assessment of place of death preference for both patient and caregiver is needed.

Genotypic Distribution and Antimicrobial Susceptibilities of Carbapenemase-Producing Enterobacteriaceae Isolated From Rectal and Clinical Samples in Korean University Hospitals Between 2016 and 2019.

BACKGROUND: The emergence of carbapenemase-producing Enterobacteriaceae (CPE) represents a major clinical problem. Recently, the occurrence of CPE has increased globally, but epidemiological patterns vary across region. We report the trends in the genotypic distribution and antimicrobial susceptibility of CPE isolated from rectal and clinical samples during a four-year period. METHODS: Between January 2016 and December 2019, 1,254 nonduplicated CPE isolates were obtained from four university hospitals in Korea. Carbapenemase genotypes were determined by multiplex real-time PCR. Antimicrobial susceptibility was profiled using the Vitek 2 system (bioMérieux, Hazelwood, MO, USA) or MicroScan Walkaway-96 system (Siemens West Sacramento, CA, USA). The proportions of carbapenemase genotypes and nonsusceptibility were analyzed using Pearson's chi-square test. RESULTS: Among the 1,254 CPE isolates, 486 (38.8%), 371 (29.6%), 357 (28.5%), 8 (0.6%), 8 (0.6%), and 24 (1.9%) were Klebsiella pneumoniae carbapenemase (KPC), oxacillinase (OXA)-48-like, New Delhi metallo-ß-lactamase (NDM), imipenemase (IMP), Verona integron-encoded metallo-ß-lactamase (VIM), and multiple producers, respectively. The predominant species was K. pneumoniae (72.6%), followed by Escherichia coli (6.5%). More than 90% of the isolates harboring KPC, NDM, and OXA-48-like were nonsusceptible to cephalosporins, aztreonam, and carbapenems. CONCLUSIONS: The impact of CPE is primarily due to KPC-, NDM-, and OXA-48-like-producing K. pneumoniae isolates. Isolates carrying these carbapenemase are mostly multidrug-resistant. Control strategies based on these genotypic distributions and antimicrobial susceptibilities of CPE isolates are required.