Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules. RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%. CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

A modified 3D RPN for detecting lung nodules on CT images that exhibited greater sensitivity and CPM than did several previously reported CAD detection models was established.

Early changes in skin surface temperature predict body temperature increases in patients with fever: A pilot study.

OBJECTIVE: To investigate the correlation between body temperature and skin surface temperature in intensive care unit patients and to identify specific indicators of skin surface temperature for early fever detection. RESEARCH METHODOLOGY/DESIGN: This pilot study was a prospective, observational investigation conducted at National Cheng Kung University Hospital in Tainan, Taiwan. A total of 54 patients admitted to the Surgical Intensive Care Unit of a tertiary hospital between April and August 2020 were included. Patients utilized the wearable device HEARThremoTM to continuously monitor skin surface temperature and heart rate. Analysis of Variance was applied to identify the association of skin surface temperature with different body temperature groups. The comparison between skin surface temperature and fever over eight time intervals was studied using a generalized estimating equation. RESULTS: In 34 patients (63 %) with a fever (≥38 °C), skin surface temperature increased (P < 0.001) when body temperature increased. The maximum skin surface temperature was significantly associated with fever 180-210 min before the fever events occurred (OR: 2.22, 95 % CI: 1.30-3.80). The mean skin surface temperature was associated with fever 120-150 min before the fever events (OR: 8.70, 95 % CI: 2.08-36.36). CONCLUSIONS: Skin surface temperature can be an important early predictive sign before the onset of fever. Continuous temperature monitoring can detect fever early and initiate treatment in advance. This study serves as a preliminary exploration in this area, laying the groundwork for future comprehensive research. IMPLICATIONS FOR CLINICAL PRACTICE: Continuous monitoring of skin surface temperature empowers nurses to swiftly detect fever, transcending conventional methods. This proactive approach allows for the early identification of physiological abnormalities, facilitating the prompt initiation of further physical assessments and relevant examinations for early treatment commencement.

Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNs.

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout (Tinitial) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model Tinitial and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller-switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time.

Sex difference in the association between excessive daytime sleepiness and health-related quality of life in community-dwelling older adults: The Yilan study, Taiwan.

OBJECTIVES: The association between excessive daytime sleepiness and health-related quality of life among older adults and at-risk individuals remains unclear. This study examined relationships between excessive daytime sleepiness and unfavorable health-related quality of life and explored the moderating effect of sex. STUDY DESIGN: This was a community-based study of adults aged 65 years or more. Excessive daytime sleepiness was defined as a score exceeding 10 on the Epworth Sleepiness Scale. Multiple logistic regression analyses were used to examine the relationships between excessive daytime sleepiness and health-related quality of life. The moderating effect of sex was examined by testing interaction terms. MAIN OUTCOME MEASURES: Health-related quality of life was measured using the Short Form 12 Health Survey, which includes a physical component summary and a mental component summary. Unfavorable health-related quality of life was defined as the lowest tertile of the scores for both components. RESULTS: In total, 3788 individuals participated. After controlling for covariates, older adults with excessive daytime sleepiness did not have an unfavorable physical component summary but were more likely to have an unfavorable mental component summary (odds ratio 1.96; 95 % confidence interval 1.47-2.61). When stratified by sex, excessive daytime sleepiness was associated with a poor physical component summary in men (odds ratio 1.77, 95 % confidence interval 1.00-3.13) but not in women. CONCLUSIONS: Excessive daytime sleepiness was associated with a poor mental component summary in both sexes; however, the association with a poor physical component summary was specific to men.

TEM1/endosialin/CD248 promotes pathologic scarring and TGF-ß activity through its receptor stability in dermal fibroblasts.

BACKGROUND: Pathologic scars, including keloids and hypertrophic scars, represent a common form of exaggerated cutaneous scarring that is difficult to prevent or treat effectively. Additionally, the pathobiology of pathologic scars remains poorly understood. We aim at investigating the impact of TEM1 (also known as endosialin or CD248), which is a glycosylated type I transmembrane protein, on development of pathologic scars. METHODS: To investigate the expression of TEM1, we utilized immunofluorescence staining, Western blotting, and single-cell RNA-sequencing (scRNA-seq) techniques. We conducted in vitro cell culture experiments and an in vivo stretch-induced scar mouse model to study the involvement of TEM1 in TGF-ß-mediated responses in pathologic scars. RESULTS: The levels of the protein TEM1 are elevated in both hypertrophic scars and keloids in comparison to normal skin. A re-analysis of scRNA-seq datasets reveals that a major profibrotic subpopulation of keloid and hypertrophic scar fibroblasts greatly expresses TEM1, with expression increasing during fibroblast activation. TEM1 promotes activation, proliferation, and ECM production in human dermal fibroblasts by enhancing TGF-ß1 signaling through binding with and stabilizing TGF-ß receptors. Global deletion of Tem1 markedly reduces the amount of ECM synthesis and inflammation in a scar in a mouse model of stretch-induced pathologic scarring. The intralesional administration of ontuxizumab, a humanized IgG monoclonal antibody targeting TEM1, significantly decreased both the size and collagen density of keloids. CONCLUSIONS: Our data indicate that TEM1 plays a role in pathologic scarring, with its synergistic effect on the TGF-ß signaling contributing to dermal fibroblast activation. Targeting TEM1 may represent a novel therapeutic approach in reducing the morbidity of pathologic scars.

Auto-suppression of Tet dioxygenases protects the mouse oocyte genome from oxidative demethylation.

DNA cytosine methylation plays a vital role in repressing retrotransposons, and such derepression is linked with developmental failure, tumorigenesis and aging. DNA methylation patterns are formed by precisely regulated actions of DNA methylation writers (DNA methyltransferases) and erasers (TET, ten-eleven translocation dioxygenases). However, the mechanisms underlying target-specific oxidation of 5mC by TET dioxygenases remain largely unexplored. Here we show that a large low-complexity domain (LCD), located in the catalytic part of Tet enzymes, negatively regulates the dioxygenase activity. Recombinant Tet3 lacking LCD is shown to be hyperactive in converting 5mC into oxidized species in vitro. Endogenous expression of the hyperactive Tet3 mutant in mouse oocytes results in genome-wide 5mC oxidation. Notably, the occurrence of aberrant 5mC oxidation correlates with a consequent loss of the repressive histone mark H3K9me3 at ERVK retrotransposons. The erosion of both 5mC and H3K9me3 causes ERVK derepression along with upregulation of their neighboring genes, potentially leading to the impairment of oocyte development. These findings suggest that Tet dioxygenases use an intrinsic auto-regulatory mechanism to tightly regulate their enzymatic activity, thus achieving spatiotemporal specificity of methylome reprogramming, and highlight the importance of methylome integrity for development.

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.

OBJECTIVES: The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. MATERIALS AND METHODS: The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task. RESULTS: The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%. CONCLUSION: Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.

Do We Need to Administer Fludrocortisone in Addition to Hydrocortisone in Adult Patients With Septic Shock? An Updated Systematic Review With Bayesian Network Meta-Analysis of Randomized Controlled Trials and an Observational Study With Target Trial Emulation.

OBJECTIVES: This systematic review and Bayesian network meta-analysis evaluated the efficacy and safety of hydrocortisone combined with fludrocortisone or hydrocortisone alone, compared with placebo in adult patients with septic shock. DATA SOURCES: By extending a prior Cochrane review, databases, including PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov , along with other relevant websites, were searched until August 31, 2023. STUDY SELECTION: Randomized controlled trials (RCTs) and observational studies using target trial emulation were included. DATA EXTRACTION: The primary outcome was short-term mortality with an emphasis on 28- or 30-day mortality as the main measure and in-hospital or ICU mortality as the nearest surrogate of this measure. Three of the most common adverse events, namely, gastroduodenal bleeding, superinfection, and hyperglycemia, were also considered. DATA SYNTHESIS: A total of 19 studies involving 95,841 patients were included. Hydrocortisone plus fludrocortisone showed the lowest short-term mortality versus placebo (odds ratio [OR]: 0.79; 95% credible interval [CrI], 0.64-0.99; number needed to treat [NNT]: 21, range: 12-500; low certainty of evidence) in terms of informative priors. The surface under the cumulative ranking curve values for hydrocortisone plus fludrocortisone, hydrocortisone alone, and placebo were 0.9469, 0.4542, and 0.0989, respectively. Consistent results were observed in RCTs alone and those using a daily 200-mg dose of hydrocortisone. Although gastroduodenal bleeding or superinfection showed no clear increase, hyperglycemia risk increased. The ORs were 0.53 for placebo versus hydrocortisone plus fludrocortisone and 0.64 for placebo versus hydrocortisone alone, with very low certainty of evidence. CONCLUSIONS: In adults with septic shock, hydrocortisone plus fludrocortisone improved short-term survival with minimal adverse events compared with hydrocortisone alone or placebo. However, these findings are not definitive due to the limited certainty of evidence and wide NNT range. Additional large-scale, placebo-controlled RCTs are needed to provide conclusive evidence.

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics.

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.

Soluble CD93 lectin-like domain sequesters HMGB1 to ameliorate inflammatory diseases.

Rationale: CD93, a C-type lectin-like transmembrane glycoprotein, can be shed in a soluble form (sCD93) upon inflammatory stimuli. sCD93 effectively enhances apoptotic cell clearance and has been proposed as an inflammatory disease biomarker. The function of sCD93 involved directly in inflammation remains to be determined. Herein, we attempted to examine the hypothesis that sCD93 might sequester proinflammatory high-mobility group box 1 protein (HMGB1), exerting anti-inflammatory properties. Methods: Different forms of soluble recombinant human CD93 (rCD93) were prepared by a mammalian protein expression system. rCD93-HMGB1 interaction was assessed using co-immunoprecipitation and solid-phase binding assays. Effects of soluble rCD93 were evaluated in HMGB1-induced macrophage and vascular smooth muscle cells (VSMC) activation and receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis, CaCl2-induced and angiotensin II-infused abdominal aortic aneurysm (AAA) formation and ovariectomized-induced osteoporosis in mice. Results: Protein binding studies revealed that soluble rCD93, via the lectin-like domain (D1), can bind to HMGB1 and intercept HMGB1-receptor interaction. Soluble rCD93 containing D1 inhibited HMGB1-induced proinflammatory cytokine production and intracellular mitogen-activated protein kinase (MAPK)/nuclear factor (NF)-κB activation in macrophages and VSMCs, thereby attenuating CaCl2-induced and angiotensin II-infused AAA models. During osteoclastogenesis, RANKL stimulated HMGB1 secretion that promoted RANKL-induced osteoclastogenesis in return. Soluble rCD93 containing D1 impeded RANKL-induced osteoclastogenic marker gene expression and intracellular MAPK/NF-κB signaling, thereby mitigating ovariectomized-induced osteoporosis. Conclusion: These findings demonstrate the therapeutic potential of soluble recombinant CD93 containing D1 in inflammatory diseases. Our study highlights a novel anti-inflammatory mechanism, i.e., sequestration of HMGB1, through which sCD93 prevents HMGB1-receptor interaction on effector cells and alleviates inflammation.

Indoor CO2 monitoring in a surgical intensive care unit under visitation restrictions during the COVID-19 pandemic.

Background: Indoor CO2 concentration is an important metric of indoor air quality (IAQ). The dynamic temporal pattern of CO2 levels in intensive care units (ICUs), where healthcare providers experience high cognitive load and occupant numbers are frequently changing, has not been comprehensively characterized. Objective: We attempted to describe the dynamic change in CO2 levels in the ICU using an Internet of Things-based (IoT-based) monitoring system. Specifically, given that the COVID-19 pandemic makes hospital visitation restrictions necessary worldwide, this study aimed to appraise the impact of visitation restrictions on CO2 levels in the ICU. Methods: Since February 2020, an IoT-based intelligent indoor environment monitoring system has been implemented in a 24-bed university hospital ICU, which is symmetrically divided into areas A and B. One sensor was placed at the workstation of each area for continuous monitoring. The data of CO2 and other pollutants (e.g., PM2.5) measured under standard and restricted visitation policies during the COVID-19 pandemic were retrieved for analysis. Additionally, the CO2 levels were compared between workdays and non-working days and between areas A and B. Results: The median CO2 level (interquartile range [IQR]) was 616 (524-682) ppm, and only 979 (0.34%) data points obtained in area A during standard visitation were ≥ 1,000 ppm. The CO2 concentrations were significantly lower during restricted visitation (median [IQR]: 576 [556-596] ppm) than during standard visitation (628 [602-663] ppm; p < 0.001). The PM2.5 concentrations were significantly lower during restricted visitation (median [IQR]: 1 [0-1] µg/m3) than during standard visitation (2 [1-3] µg/m3; p < 0.001). The daily CO2 and PM2.5 levels were relatively low at night and elevated as the occupant number increased during clinical handover and visitation. The CO2 concentrations were significantly higher in area A (median [IQR]: 681 [653-712] ppm) than in area B (524 [504-547] ppm; p < 0.001). The CO2 concentrations were significantly lower on non-working days (median [IQR]: 606 [587-671] ppm) than on workdays (583 [573-600] ppm; p < 0.001). Conclusion: Our study suggests that visitation restrictions during the COVID-19 pandemic may affect CO2 levels in the ICU. Implantation of the IoT-based IAQ sensing network system may facilitate the monitoring of indoor CO2 levels.

A Trusted Reputation Management Scheme for Cross-Chain Transactions.

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node's reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.

Ocean-Surface Wave Measurements Using Scintillation Theories on Seaborne Software-Defined GPS and SBAS Reflectometry Observations.

In this study, a low-cost, software-defined Global Positioning System (GPS) and Satellite-Based Augmentation System (SBAS) Reflectometry (GPS&SBAS-R) system has been built and proposed to measure ocean-surface wave parameters on board the research vessel New Ocean Researcher 1 (R/V NOR-1) of Taiwan. A power-law, ocean-wave spectrum model has been used and applied with the Small Perturbation Method approach to solve the electromagnetic wave scattering problem from rough ocean surface, and compared with experimental seaborne GPS&SBAS-R observations. Meanwhile, the intensity scintillations of high-sampling GPS&SBAS-R signal acquisition data are thought to be caused by the moving of rough surfaces of the targeted ocean. We found that each derived scintillation power spectrum is a Fresnel-filtering result on ocean-surface elevation fluctuations and depends on the First Fresnel Zone (FFZ) distance and the ocean-surface wave velocity. The determined ocean-surface wave speeds have been compared and validated against nearby buoy measurements.

Chondrocyte Thrombomodulin Protects against Osteoarthritis.

Osteoarthritis (OA) is a prevalent form of arthritis that affects over 32.5 million adults worldwide, causing significant cartilage damage and disability. Unfortunately, there are currently no effective treatments for OA, highlighting the need for novel therapeutic approaches. Thrombomodulin (TM), a glycoprotein expressed by chondrocytes and other cell types, has an unknown role in OA. Here, we investigated the function of TM in chondrocytes and OA using various methods, including recombinant TM (rTM), transgenic mice lacking the TM lectin-like domain (TMLeD/LeD), and a microRNA (miRNA) antagomir that increased TM expression. Results showed that chondrocyte-expressed TM and soluble TM [sTM, like recombinant TM domain 1 to 3 (rTMD123)] enhanced cell growth and migration, blocked interleukin-1ß (IL-1ß)-mediated signaling and protected against knee function and bone integrity loss in an anterior cruciate ligament transection (ACLT)-induced mouse model of OA. Conversely, TMLeD/LeD mice exhibited accelerated knee function loss, while treatment with rTMD123 protected against cartilage loss even one-week post-surgery. The administration of an miRNA antagomir (miR-up-TM) also increased TM expression and protected against cartilage damage in the OA model. These findings suggested that chondrocyte TM plays a crucial role in counteracting OA, and miR-up-TM may represent a promising therapeutic approach to protect against cartilage-related disorders.

Feasibility of simultaneous development of laparoscopic and robotic pancreaticoduodenectomy.

Laparoscopic (LPD) and robotic pancreaticoduodenectomy (RPD) are both challenging procedures. The feasibility and safety of simultaneously developing LPD and RPD remain unreported. We retrospectively reviewed the data of patients undergoing LPD or RPD between 2014 and 2021. A total of 114 patients underwent minimally invasive pancreaticoduodenectomy (MIPD): 39 LPDs and 75 RPDs. The learning process of LPD and RPD were similar. The cutoff points of the learning curve were LPD, 13th patient (the 27th patient of MIPD), and RPD, 18th patient (the 31st patient of MIPD) according the cumulative sum analysis of operative time. A decrease in the operative time was associated with the case sequence (p < 0.001) but not with the surgical approach (p = 0.36). The overall surgical outcomes were comparable between both the LPD and RPD groups. When evaluating the learning curve impact on MIPD, LPD had higher major complication (≧ Clavien-Dindo grade III), bile leak and wound infection rates in the pre-learning curve phase than those in the after-learning curve phase, while RPD had similar surgical outcomes between two phases. Simultaneous development of LPD and RPD is feasible and safe for experienced surgeons, with similar learning process and comparable surgical outcomes.

Outcomes of the "BRCA Quality Improvement Dissemination Program": An initiative to improve patient receipt of cancer genetics services at five health systems.

OBJECTIVE: A quality improvement initiative (QII) was conducted with five community-based health systems' oncology care centers (sites A-E). The QII aimed to increase referrals, genetic counseling (GC), and germline genetic testing (GT) for patients with ovarian cancer (OC) and triple-negative breast cancer (TNBC). METHODS: QII activities occurred at sites over several years, all concluding by December 2020. Medical records of patients with OC and TNBC were reviewed, and rates of referral, GC, and GT of patients diagnosed during the 2 years before the QII were compared to those diagnosed during the QII. Outcomes were analyzed using descriptive statistics, two-sample t-test, chi-squared/Fisher's exact test, and logistic regression. RESULTS: For patients with OC, improvement was observed in the rate of referral (from 70% to 79%), GC (from 44% to 61%), GT (from 54% to 62%) and decreased time from diagnosis to GC and GT. For patients with TNBC, increased rates of referral (from 90% to 92%), GC (from 68% to 72%) and GT (81% to 86%) were observed. Effective interventions streamlined GC scheduling and standardized referral processes. CONCLUSION: A multi-year QII increased patient referral and uptake of recommended genetics services across five unique community-based oncology care settings.

Differences in the Clinical Characteristics and 1-Year Mortality Rates of Patients with Dermatomyositis with anti-Jo-1 and anti-MDA5 Antibodies.

Objective: Patients with anti-Jo-1 antibodies (Abs) and anti-melanoma differentiation-associated protein 5 (MDA5) Abs are at a higher risk of interstitial lung disease (ILD) and have a mortality rate higher than that of patients with anti-Jo-1 Abs. This study investigated differences in the clinical characteristics and prognosis of patients with anti-Jo-1 Abs and anti-MDA5 Abs with dermatomyositis (DM). Methods: We retrospectively reviewed the medical records of 38 patients with DM from January 2000 to December 2021. The patients were divided into anti-Jo-1 Abs and anti-MDA5 Abs groups. The basic demographic data, clinical manifestations, and 1-year mortality rates of the groups were compared. Results: Among the 38 patients, 30 were anti-Jo-1-Abs positive and 8 patients were anti-MDA5 Aba positive. The patients with anti-MDA5 Abs presented with more apparent cutaneous symptoms and aggressive pulmonary manifestations than did those with anti-Jo-1 Abs. The mortality rate in the anti-MDA5 Abs group (1.95/person-year (PY)) was much higher than that in anti-Jo-1 Abs group (0.094/PY), and most of the mortalities occurred within the first 1-3 months of follow-up. Conclusion: Distinct cutaneous and pulmonary manifestations were observed in the anti-Jo-1 Abs and anti-MDA5 Abs groups. The mortality rate in the anti-MDA5 Abs group was significantly higher than that in the anti-Jo-1 Abs group. Early recognition is crucial to ensuring higher chances of survival for patients with anti-MDA5 Abs.

Minimally invasive approaches increase postoperative complications in obese patients undergoing pancreaticoduodenectomy during the initial development period: a propensity score matching study.

BACKGROUND: Obesity increases surgical risks in various abdominal surgeries and its impact on open pancreaticoduodenectomy (OPD) and minimally invasive pancreaticoduodenectomy (MIPD) remains unknown. This study aimed to compare the surgical outcomes of OPD and MIPD in obese and non-obese patients by propensity score matching (PSM) analysis during the implementation of MIPD. METHODS: We retrospectively reviewed all pancreaticoduodenectomies from December 2014 to May 2021. Obesity was defined as body mass index > 25 kg/m2 according to World Health Organization International Obesity Task Force. PSM was used to minimize the selection bias of MIPD. RESULTS: Among 462 pancreaticoduodenectomies (339 OPDs, 123 MIPDs), there were 313 patients in the non-obese group (MIPD: 78, OPD: 235) and 149 patients in the obese group (MIPD: 45, OPD: 104). After PSM, there were 70 MIPD/106 OPD patients in the non-obese group and 38 MIPD/54 OPD patients in the obese group. The obese MIPD patients had more fluid collection (36.8% vs 9.8%, p = 0.002), a higher Clavien-Dindo (CD) grade (p = 0.007), more major complications (42.1% vs 14.8%, p = 0.004), and longer operative times (306 min vs 264 min, p < 0.001) than the obese OPD patients. The non-obese MIPD patients had lower CD grades (p = 0.02), longer operative times (294 vs 264 min, p < 0.001), and less blood loss (100 mL vs 200 mL) than the non-obese OPD patients. MIPD was a strong predictor of major complication (CD ≥ 3) in obese patients (odds ratio 3.11, 95% CI: 1.40-6.95, p = 0.005). CONCLUSIONS: Minimally invasive approaches deteriorate the CD grade, fluid collection, and major complications in obese patients undergoing pancreaticoduodenectomy during the initial development period. Non-obese patients may benefit from MIPD over OPD in terms of less blood loss and lower CD grades. The impact of BMI on MIPD should be considered when assessing the surgical risks.