The establishment of a multiple myeloma clinical registry in the Asia-Pacific region: The Asia-Pacific Myeloma and Related Diseases Registry (APAC MRDR).

BACKGROUND: Multiple myeloma (MM) is the second most common haematological cancer worldwide. Along with related diseases including monoclonal gammopathy of undetermined significance (MGUS), plasma cell leukaemia (PCL) and plasmacytoma, MM incidence is rising, yet it remains incurable and represents a significant disease burden. Clinical registries can provide important information on management and outcomes, and are vital platforms for clinical trials and other research. The Asia-Pacific Myeloma and Related Diseases Registry (APAC MRDR) was developed to monitor and explore variation in epidemiology, treatment regimens and their impact on clinical outcomes across this region. Here we describe the registry's design and development, initial data, progress and future plans. METHODS: The APAC MRDR was established in 2018 as a multicentre collaboration across the Asia-Pacific, collecting prospective data on patients newly diagnosed with MM, MGUS, PCL and plasmacytoma in Korea, Singapore, Malaysia and Taiwan, with China recently joining. Development of the registry required a multidisciplinary team of clinicians, researchers, legal and information technology support, and financial resources, as well as local clinical context from key opinion leaders in the APAC region. Written informed consent is obtained and data are routinely collected throughout treatment by hospital staff. Data are stored securely, meeting all local privacy and ethics requirements. Data were collected from October 2018 to March 2024. RESULTS: Over 1700 patients from 24 hospitals have been enrolled onto the APAC MRDR to date, with the majority (86%) being newly diagnosed with MM. Bortezomib with an immunomodulatory drug was most frequently used in first-line MM therapy, and lenalidomide-based therapy was most common in second-line. Establishment and implementation challenges include regulatory and a range of operational issues. CONCLUSION: The APAC MRDR is providing 'real-world' data to participating sites, clinicians and policy-makers to explore factors influencing outcomes and survival, and to support high quality studies. It is already a valuable resource that will continue to grow and support research and clinical collaboration in MM and related diseases across the APAC region.

Detection of Feline Coronavirus in Bronchoalveolar Lavage Fluid from Cats with Atypical Lower Airway and Lung Disease: Suspicion of Virus-Associated Pneumonia or Pneumonitis.

The premortem understanding of the role of feline coronavirus (FeCoV) in the lungs of cats is limited as viruses are seldom inspected in the bronchoalveolar lavage (BAL) specimens of small animal patients. This study retrospectively analyzed the prevalence of FeCoV in BAL samples from cats with atypical lower airway and lung disease, as well as the clinical characteristics, diagnostic findings, and follow-up information. Of 1162 clinical samples submitted for FeCoV RT-nPCR, 25 were BAL fluid. After excluding 1 case with chronic aspiration, FeCoV was found in 3/24 (13%) BAL specimens, with 2 having immunofluorescence staining confirming the presence of FeCoV within the cytoplasm of alveolar macrophages. The cats with FeCoV in BAL fluid more often had pulmonary nodular lesions (66% vs. 19%, p = 0.14) and multinucleated cells on cytology (100% vs. 48%, p = 0.22) compared to the cats without, but these differences did not reach statistical significance due to the small sample size. Three cats showed an initial positive response to the corticosteroid treatment based on the clinical signs and radiological findings, but the long-term prognosis varied. The clinical suspicion of FeCoV-associated pneumonia or pneumonitis was raised since no other pathogens were found after extensive investigations. Further studies are warranted to investigate the interaction between FeCoV and lung responses in cats.

Gut Microbiome and Metabolic and Immune Indices in Males with or without Evidence of Metabolic Dysregulation.

Background: The contributions of the gut microbiota to obesity and metabolic disease represent a potentially modifiable factor that may explain variation in risk between individuals. This study aimed to explore relationships among microbial composition and imputed functional attributes, a range of soluble metabolic and immune indices, and gene expression markers in males with or without evidence of metabolic dysregulation (MetDys). Methods: This case-control study included healthy males (n=15; 41.9±11.7 years; body mass index [BMI], 22.9±1.2 kg/m2) and males with evidence of MetDys (n=14; 46.6±10.0 years; BMI, 35.1±3.3 kg/m2) who provided blood and faecal samples for assessment of a range of metabolic and immune markers and microbial composition using 16S rRNA gene sequencing. Metagenomic functions were imputed from microbial sequence data for analysis. Results: In addition to elevated values in a range of traditional metabolic, adipokine and inflammatory indices in the MetDys group, 23 immunomodulatory genes were significantly altered in the MetDys group. Overall microbial diversity did not differ between groups; however, a trend for a higher relative abundance of the Bacteroidetes (P=0.06) and a lower relative abundance of the Verrucomicrobia (P=0.09) phyla was noted in the MetDys group. Using both family- and genera-level classifications, a partial least square discriminant analysis revealed unique microbial signatures between the groups. Conclusion: These findings confirm the need for ongoing investigations in human clinical cohorts to further resolve the relationships between the gut microbiota and metabolic and immune markers and risk for metabolic disease.

GPS-Free Wireless Precise Positioning System for Automatic Flying and Landing Application of Shipborne Unmanned Aerial Vehicle.

This research is dedicated to developing an automatic landing system for shipborne unmanned aerial vehicles (UAVs) based on wireless precise positioning technology. The application scenario is practical for specific challenging and complex environmental conditions, such as the Global Positioning System (GPS) being disabled during wartime. The primary objective is to establish a precise and real-time dynamic wireless positioning system, ensuring that the UAV can autonomously land on the shipborne platform without relying on GPS. This work addresses several key aspects, including the implementation of an ultra-wideband (UWB) circuit module with a specific antenna design and RF front-end chip to enhance wireless signal reception. These modules play a crucial role in achieving accurate positioning, mitigating the limitations caused by GPS inaccuracy, thereby enhancing the overall performance and reception range of the system. Additionally, the study develops a wireless positioning algorithm to validate the effectiveness of automatic landing on the shipborne platform. The platform's wave vibration is considered to provide a realistic landing system for shipborne UAVs. The UWB modules are practically installed on the shipborne platform, and the UAV and the autonomous three-body vessel are tested simultaneously in the outdoor open water space to verify the functionality, precision, and adaptability of the proposed UAV landing system. Results demonstrate that the UAV can autonomously fly from 200 m, approach, and automatically land on the moving shipborne platform without relying on GPS.

Characteristics and Outcomes of Older Patients With Classical Hodgkin Lymphoma: An Australasian Lymphoma Alliance, and Lymphoma and Related Diseases Registry Study.

BACKGROUND: There is no standard front-line therapy for older patients with classical Hodgkin lymphoma (cHL). We analyzed the clinical presentation and front-line management of older Australian patients with cHL and explored factors associated with unplanned hospital admission and survival. METHODS: Patients aged ≥ 61 years and diagnosed between 2011 and 2020, were retrospectively identified through the Lymphoma and Related Diseases Registry (LaRDR) and Australasian Lymphoma Alliance (ALA) institutional databases. Descriptive statistics and Kaplan-Meier survival analyses were performed using STATA-v17. RESULTS: 195 patients were identified, 72 from LaRDR,123 from ALA. Median age of the combined cohort was 72 years (range 61-93); 56.4% male, 35.3% had stage I-II, bulk present in 9.2%, 33.9% had extra-nodal disease and 48.2% had B-symptoms. Chemotherapy was commenced in 91.3% of patients, with an anthracycline-based regimen used in 81%. Median number of cycles given for stage I-II was 2 and for stage III-IV was 6. Radiotherapy was administered in 26.2% of patients. A complete remission to front-line chemotherapy was achieved in 60.7% of patients. During front-line therapy in the ALA cohort, 89 unplanned hospitalizations occurred in 58 patients, with infection accounting for 59.6% of admissions. Treatment-related mortality was 5.2%. Only performance status and anthracycline use correlated with unplanned hospitalizations. Estimated 2-year progression free survival was 63.7% and 2-year overall survival was 71.2%. Anthracycline use and younger age were independently associated with improved survival. CONCLUSION: The management of older patients with cHL in Australia is diverse but aligns with international data. Anthracycline-based therapy improved survival but resulted in frequent unplanned hospitalizations.

A correlation-based network for biomarker discovery in obesity with metabolic syndrome.

BACKGROUND: Obesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarker profile has been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to identify existing patterns of immune-microbiome interactions in obesity. RESULTS: The current study performed correlation-based network analysis on five different datasets obtained from 11 obese with metabolic syndrome (MetS) and 12 healthy weight men. These datasets included: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese with MetS group had a denser network (total number of edges, n = 369) compared to the healthy network (n = 299). Within the obese with MetS network, biomarkers from the immune cell abundance group was found to be correlated to biomarkers from all four other datasets. Conversely in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. These observations suggest high involvement of immune cells in obese with MetS individuals. There were also three key hubs found among immune cells in the obese with MetS networks involving regulatory T cells, neutrophil and cytotoxic cell abundance. No hubs were present in the healthy network. CONCLUSION: These results suggest a more complex interaction of inflammatory markers in obesity, with high connectivity of immune cells in the obese with MetS network compared to the healthy network. Three key hubs were identified in the obese with MetS network, involving Treg, neutrophils and cytotoxic cell abundance. Compared to a t-test, the network approach offered more meaningful results when comparing obese with MetS and healthy weight individuals, demonstrating its superiority in exploratory analysis.

Selection of microbial biomarkers with genetic algorithm and principal component analysis.

BACKGROUND: Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to determine whether individuals can be clustered into two classification groups based on pre-determined criteria: control and disease group. However, a combination of other components may exist which better distinguish diseased individuals from healthy controls. Genetic algorithms (GAs) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and a genetic algorithm (GA) for identifying sets of bacterial species associated with obesity and metabolic syndrome (Mets). RESULTS: The prediction models built using the combination of principal components (PCs) selected by GA were compared to the models built using the top PCs that explained the most variance in the sample and to models built with selected original variables. The advantages of combining PCA with GA were demonstrated. CONCLUSIONS: The proposed algorithm overcomes the limitation of PCA for data analysis. It offers a new way to build prediction models that may improve the prediction accuracy. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.