Tracking Database Research Articles

Divert More Attention to Vision-Language Object Tracking.

Multimodal vision-language (VL) learning has noticeably pushed the tendency toward generic intelligence owing to emerging large foundation models. However, tracking, as a fundamental vision problem, surprisingly enjoys less bonus from recent flourishing VL learning. We argue that the reasons are two-fold: the lack of large-scale vision-language annotated videos and ineffective vision-language interaction learning of current works. These nuisances motivate us to design more effective vision-language representation for tracking, meanwhile constructing a large database with language annotation for model learning. Particularly, in this paper, we first propose a general attribute annotation strategy to decorate videos in six popular tracking benchmarks, which contributes a large-scale vision-language tracking database with more than 23,000 videos. We then introduce a novel framework to improve tracking by learning a unified-adaptive VL representation, where the cores are the proposed asymmetric architecture search and modality mixer (ModaMixer). To further improve VL representation, we introduce a contrastive loss to align different modalities. To thoroughly evidence the effectiveness of our method, we integrate the proposed framework on three tracking methods with different designs, i.e., the CNN-based SiamCAR (Guo et al. 2020), the Transformer-based OSTrack (Ye et al. 2022), and the hybrid structure TransT (Chen et al. 2021). The experiments demonstrate that our framework can significantly improve all baselines on six benchmarks. Besides empirical results, we theoretically analyze our approach to show its rationality. By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking with diversified multimodal messages.

Trends in Specialty Training and National Institutes of Health Funding Among Surgeon-Scientists

Purpose: To determine if dual-degree training [ie, completion of a National Institutes of Health (NIH)-funded MD/PhD program], among other professional development and demographic variables, predicted academic productivity (eg, K-to-R conversion, number of publications, etc.) among early-career surgeon-scientists. Methods: We analyzed publicly available data from the National MD/PhD Program Outcomes Study and the Association of American Medical Colleges Graduate Medical Education Track database to identify trends in the number and proportion of MD/PhD graduates pursuing surgical specialties. NIH Research Portfolio Online Reporting Tool Expenditures and Results was interrogated to identify a cohort of early-career academic surgeon-scientists receiving K-awards from 2011 to 2021. Results: The total number of MD/PhD program graduates completing Graduate Medical Education training increased each decade after the Medical Scientist Training Program was established by the National Institute of General Medical Sciences, but the proportion completing surgical specialties did not change significantly (P = 0.96) from 1965 to 2014. More recent residency match trends demonstrate an increase in both the proportion and number of MD/PhD graduates entering surgical specialties, with 21.5% entering surgical residency training in 2020. Among 476 early-career academic surgeon-scientists receiving K-awards at 70 institutions, 27% were faculty members at only 4 universities, suggesting that federally funded surgeon-scientists are concentrated at a small number of institutions. Although MD/PhD graduates represented only 2.3% of active surgical residents from 2011 to 2020, they constituted a much higher fraction of K-awardees (29%). Of 296 surgeon-scientists who completed K-awards, 35% successfully obtained an R01-equivalent award. Conclusions: These findings emphasize the need for comprehensive career development and institutional resources to support early-career surgeon-scientists.

Efficacy of acupuncture in reducing accidental injury risk in stroke patients: A national-scale cohort study

BackgroundFalls among stroke patients pose a significant health concern, leading to injuries, reduced mobility, and functional limitations. Recent research has suggested the potential benefits of acupuncture in enhancing balance and motor function in stroke patients. However, large-scale, long-term studies on acupuncture's role in reducing the risk of accidental injuries are scarce. This study aimed to evaluate acupuncture's effectiveness in decreasing the risk of accidental injuries in stroke patients. Materials and methodsThis study employed a large-scale cohort design, utilizing data from the 2000 Longitudinal Generation Tracking Database (LGTD 2000) in Taiwan, covering two million individuals. The cohort comprised stroke patients hospitalized between 2001 and 2012, with follow-up periods of at least six years to estimate accidental injury incidence and risk. Acupuncture treatments during both inpatient and outpatient phases after stroke diagnosis were included. Accidental injury data, including vehicular accidents and falls, were extracted from specialized hospitalization records. Hazard ratios were calculated, adjusting for variables like comorbidities, hypnotic medication usage, and demographic factors. ResultA total of 108,196 newly diagnosed stroke patients were included in the final cohort, with 42,083 receiving acupuncture treatment and 66,113 not receiving acupuncture. The acupuncture group exhibited a significantly lower incidence rate of accidental injuries (10.2 per 1,000 person-years) compared to the non-acupuncture group (15.8 per 1,000 person-years). After adjusting for various covariates, the acupuncture group had a reduced risk of accidental injuries with an adjusted hazard ratio (aHR) of 0.74 (95 % CI: 0.51–0.92). Subgroup analyses revealed consistent protective effects of acupuncture across different patient profiles and treatment characteristics, including age, sex, comorbidities, and hypnotic medication usage. ConclusionThis study provides evidence supporting the potential of acupuncture as an adjunctive therapy to reduce the risk of accidental injuries among stroke patients. Regardless of stroke subtype, age, or comorbidity burden, acupuncture was associated with a lower risk of accidental injuries.

Unveiling the Urban Impact on Mesoscale Convective System Rainfall in the Pearl River Delta Urban Agglomeration Under Typical Synoptic Backgrounds

AbstractMesoscale convective systems (MCSs), the primary drivers of extreme rainfall over the Pearl River Delta (PRD) urban agglomeration, are strongly influenced by synoptic circulations and local geographical environments, including water bodies and topography. However, the urban impact on MCS rainfall under various synoptic backgrounds remain inadequately understood. Using a 20‐year high‐resolution MCS tracking database and self‐organizing map clustering, three typical backgrounds for MCSs, namely weak monsoon‐like, strong monsoon‐like, and low‐pressure system (Types‐1 to 3), impacting the PRD are identified. These backgrounds exhibit pronounced disparities in MCS tracks and temporal variations as well as rainfall distributions. Urban heat island (UHI) significantly alters the spatial patterns under Types‐1 and 2. Specifically, under weak UHI condition, MCS rainfall typically occurs offshore in the morning and shifts inland in the afternoon driven by the land‐sea breeze. However, UHI modifies the low‐level thermal structure, leading to anomalous convergence and instability, which causes morning rainfall to concentrate near coastal cities, while afternoon rainfall expands further inland to the northern rural region. Additionally, the strong southwesterly winds associated with Type‐2 enhance the interaction between topography and urban impact, resulting in even higher rainfall anomalies (+28.9%) over the northeastern region. The findings highlight the crucial role of urban impact and their synergistic effect with synoptic backgrounds and other land surface processes on MCSs.

Comparison of Ante-Mortem Clinical Diagnosis and Final Autopsy Diagnosis: Experience from a Single Academic Centre in Pretoria, South Africa.

Background/Objectives: There seems to be a global reduction in the number of clinical post-mortems requested and performed worldwide, suggesting a decreasing need for post-mortem examinations. Despite advances in medical technology, autopsies remain a relevant tool to determine cause of death. Methods: A total of 276 post-mortem results were extracted from the NHLS lab track database, of which only 152 were included in this study. Discrepancies between ante and post-mortem diagnoses were evaluated using the Goldman classification. Data were analysed using STATA-18. Results: The sample consisted largely of females (n = 101, 66.45%) aged 30 and above (n = 58, 33.80%), with a mean age of 28.3. Of the 152 samples analysed, 60% (n = 92) of all postmortems showed a correlation between ante- and post-mortem diagnoses. However, 29.1% (n = 45) of cases showed major discrepancies which could have been prevented if correct diagnoses were made. Metabolic diseases were most frequently misdiagnosed (p = 0.020), with more cases of Class I discrepancies than Class V discrepancies (15.5% (n = 7) vs. 2.1% (n = 2), respectively. Additionally, infections (n = 59; 39%) were the most common cause of death. Conclusions: Even with marked improvements in diagnostic technology, a post-mortem examination is a necessary quality control tool that can be used to verify cause of death, and thus improve clinical practice.

Landscape connectivity for African elephants in the world's largest transfrontier conservation area: A collaborative, multi‐scalar assessment

Abstract Landscape connectivity operates at a variety of scales, depending on the geography of the area in question and the focal species or ecological process under consideration. Most connectivity studies, however, are typically focused on a single scale, which in the case of resistance‐based connectivity modelling, is often the entire landscape or protected area (PA) network. This large, single‐scale focus may miss areas that are important for connectivity at smaller scales and that can be documented via observed animal movements without resorting to landscape‐wide statistical modelling and extrapolation approaches. Here, we characterize landscape connectivity at three different scales (local/micro, inter‐PA, and landscape‐wide/macro), using observed animal movements rather than conventional resistance surface models, to produce a connectivity conservation blueprint for African elephants (Loxodonta africana) in the Kavango‐Zambezi transfrontier conservation area (KAZA) in southern Africa. This analysis is based on an extensive, high‐resolution GPS tracking database comprising approximately 4 million GPS locations from nearly 300 tagged elephants and their associated herds. Our results show that high‐fidelity elephant use of micro‐corridors is typically—though not exclusively—related to directed movements towards water, often amidst heavy anthropogenic presence. Movement pathways that connected KAZA's core protected areas were longer and variable, with some channelled into narrow areas of use and others more dispersed across larger sub‐landscapes. At the largest scale, a network analysis incorporating all used landscape grid cells revealed several clusters of large‐scale movement corridors that connected distant parts of KAZA. Synthesis and applications: Our three scales of analyses reveal disparate geographical priorities for connectivity conservation that collectively could help ensure the functional connectivity of KAZA for its largest inhabitants. Each scale will require its own set of inter‐related conservation interventions, while further research into areas with sparse data collection, and other species of conservation concern, could reveal additional connectivity priorities at each scale.

Integrating traditional Chinese Medicine reduced the risk of hip fracture in patients with dementia

Background/purposeThe high incidence of hip fracture is an important problem among dementia patients because of their higher risk of falls and balance deficits due to a lack of physical activity. The aim of this study was to investigate whether traditional Chinese medicine (TCM) therapy could reduce the risk of hip fracture in dementia patients. MethodsWe identified 38,071 patients who were first diagnosed with dementia from January 1, 2000, to December 31, 2017, from the database of the 2000 Longitudinal Generation Tracking Database (LGTD 2000) provided by the Health and Welfare Data Science Center (HWDC) in Taiwan. Patients who received TCM treatment after the initial diagnosis of dementia were assigned to the TCM group, and patients who never received TCM treatment were assigned to the non-TCM group. After performing 1:1 propensity score matching (1:1) based on age, sex, comorbidities and medication between the TCM and non-TCM groups, there were 902 patients in each group. Patients were followed up to December 31, 2018, and incidences of hip fracture after the initial diagnosis of dementia between the two groups were compared with Cox regression analysis. ResultsNinety-four patients in the non-TCM group (10.42%) and 58 patients in the TCM group (6.43%) suffered from hip fracture during the follow-up period. Patients in the TCM group had a lower incidence of hip fracture than those in the non-TCM group (adjusted hazard ratio = 0.54, 95% confidence interval = 0.38–0.76). ConclusionsIntegrating TCM health care for dementia patients might reduce the risk of hip fracture.

Ten-Year Follow-Up of Mitral Valve Replacement With the Epic Porcine Valve in a Medicare Population

BackgroundBioprosthetic surgical mitral valve replacement (SMVR) remains an important treatment option in the era of transcatheter valve interventions. This study presents 10-year clinical outcomes of Medicare beneficiaries who underwent SMVR with a contemporary low-profile mitral porcine valve. MethodsThis was a single-arm observational study using Medicare fee-for-service claims data. Deidentified patients undergoing SMVR with the Epic mitral valve (Abbott) in the United States between January 1, 2008 and December 31, 2019 were selected by International Classification of Diseases, Ninth and Tenth Revision procedure codes and then linked to a manufacturer device tracking database. All-cause mortality, heart failure (HF) rehospitalization, and mitral valve reintervention (surgical or transcatheter valve-in-valve) were evaluated at 10 years by using the Kaplan-Meier method. ResultsAmong 75,739 Medicare beneficiaries undergoing SMVR during the study period, 14,015 received the Epic mitral valve (Abbott), 76.5% (10,720) of whom had underlying HF. The mean age was 74 ± 8 years. Survival at 10 years in patients without preoperative HF was 40.4% (95% CI, 37.4%-43.4%) compared with 25.4% (95% CI ,23.8%-27.0%) for patients with HF (P < .001). The 10-year freedom from HF rehospitalization was 51.3% (95% CI, 49.4%-53.1%). Freedom from mitral valve reintervention was 91.4% (95% CI, 89.7%-92.7%) at 10 years. ConclusionsThis real-world nationwide study of Medicare beneficiaries receiving the Epic mitral valve demonstrates >90% freedom from all-cause valve reintervention and >50% freedom from HF rehospitalization at 10 years after implantation. Long-term survival and HF rehospitalization in this population with mitral valve disease treated with SMVR was found to be affected by underlying HF.

Does policy that provides choice in athletic footwear affect musculoskeletal injury risk in US Coast Guard recruits?

IntroductionMusculoskeletal injuries (MSKIs) are ubiquitous during initial entry military training, with overuse injuries the most common. A common injury mechanism is running, an activity that is integral to US Coast...

New insights on the interannual surface mass balance variability on the South Shetland Islands glaciers, northerly Antarctic Peninsula

Few studies have assessed a comprehensive understanding of how the seasonal and interannual variability and trends of the surface mass balance (SMB), including the influence of atmospheric river (ARs), are governed by the climate on the South Shetland Islands (SSI) glaciers located in the northerly Antarctic Peninsula (AP). To address this gap, we comprehensively analyzed the correlations and regressions between seasonal and annual SMB with regional to global climate indices and a state-of-the-art AR tracking database from 1980 to 2019. The daily and monthly SMB was obtained from two physical glaciological models, which was verified against 19 years of annual and seasonal glacier-wide SMB observations available in three glaciers (Johnsons, Hurd, and Bellingshausen), showing a good ability to capture interannual and seasonal variability. Results indicate a low dependence of the SMB on main atmospheric modes of variability (e.g., El Niño-Southern Oscillation and the Southern Annular Mode), and a moderate dependence on regional climate indices based on atmospheric pressure anomalies and sea surface temperature anomalies over the Drake Passage. Furthermore, our findings reveal that ARs have different effects on the SMB depending on the season. For example, winter ARs tend to boost accumulation due to increased snowfall, while summer ARs tend to intensify surface melting due to increased sensible heat flux. Our study highlights the Drake Passage as a key region that has the potential to influence the interannual and seasonal variability of the SMB and other climate variables, such as air temperature and snowfall over the SSI. We suggest that future work should consider this region to better understand the past, present and future climate changes on the SSI and surrounding areas.

A comprehensive database of human and livestock fecal microbiome for community-wide microbial source tracking: a case study in South Korea

Fecal waste from livestock farms contains numerous pathogens, and improperly managed waste may flow into water bodies, causing water-borne diseases. Along with the popularization of high-throughput technologies, community-wide microbial source-tracking methods have been actively developed in recent years. This study aimed to construct a comprehensive fecal microbiome database for community-wide microbial source tracking and apply the database to identify contamination sources in the Miho River, South Korea. Total DNA was extracted from the samples, and the 16 S rRNA gene was amplified to characterize the microbial communities. The fecal microbiome database was validated by developing machine-learning models that predict host species based on microbial community structure. All machine learning models developed in this study showed high performance, where the area under the receiver operating characteristic curve was approximately 1. Community-wide microbial source tracking results showed a higher contribution of fecal sources to the contamination of the main streams after heavy rain. In contrast, the contribution of fecal sources remained comparatively stable in tributaries after rainfall. Considering that farms are more concentrated upstream of tributaries compared to the main streams, this result implies that the pathway for manure contaminants to reach the main streams could be groundwater rather than surface runoff. Systematic monitoring of the water quality, which encompasses river water and groundwater, should be conducted in the future. In addition, continuous efforts to identify and plug abandoned wells are necessary to prevent further water contamination.

ExMove: An open-source toolkit for processing and exploring animal-tracking data in R.

Ongoing technological advances have led to a rapid increase in the number, type and scope of animal-tracking studies. In response, many software tools have been developed to analyse animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre-processing steps are often time-consuming and involve a steep learning curve but are crucial for the creation of high-quality, standardised and shareable data. Moreover, decisions made at this early stage can substantially influence subsequent analyses, and in the current age of reproducibility crisis, the transparency of this process is vital. Here we present an open-access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases (found here: https://github.com/ExMove/ExMove). The toolkit comprises well-documented and flexible code to facilitate data processing and user understanding, both of which can increase user confidence and improve the uptake of sharing open and reproducible code. Additionally, we provide an overview website (found here: https://exmove.github.io/) and a Shiny app to help users visualise tracking data and assist with parameter determination during data cleaning. The toolkit is generalisable to different data formats and device types, uses modern 'tidy coding' practices, and relies on a few well-maintained packages. Among these, we perform spatial manipulations using the package sf. Overall, by collating all required steps from data collection to archiving on open access databases into a single, robust pipeline, our toolkit provides a valuable resource for anyone conducting animal movement analyses and represents an important step towards increased standardisation and reproducibility in animal movement ecology.

Oncologic outpatient specialty palliative care referral utilization: A single institution quality improvement project.

e23205 Background: ASCO recommends that late-stage and/or recurrent disease patients with a prognosis of 6-24 months receive specialty palliative care within eight weeks of diagnosis. Prior studies suggest only 30% of eligible patients are referred to outpatient palliative care. Little is known about existing referral patterns to direct quality improvement initiatives. Our aim was to characterize outpatient palliative care referral sources, timeliness, and completion rates across our cancer center. More broadly, this investigation may serve as a model for outpatient palliative care utilization improvement at other cancer centers. Methods: A single institution retrospective quality improvement study was conducted of patients in the seven oncology subspecialties with the most palliative care referrals from 2019 – 2022. Data collected included demographics, referral source, and utilization metrics. Outcomes were rate of referral completion and time from referral to first palliative care visit, hospice enrollment, and/or death. Descriptive statistics were performed. Results: 1671 outpatient specialty palliative care referrals were placed. 57% were initiated by medical oncologists, 21% by inpatient palliative care consulting service at hospital discharge, and 14% by surgical oncology. 27% of referred patients did not schedule an appointment. Gynecologic and breast cancer patients had the highest rates of being scheduled. Median time from referral to appointment was 20 days (interquartile range 9 – 30 days). Reasons patients did not utilize palliative care included transition to hospice (41%), lack of interest (41%), or death prior to visit (7%). 79% of patients seen by palliative care who died were enrolled in hospice. Median time from palliative care referral to hospice enrollment was 66 days (IQR 22 – 164 days), and median time from hospice enrollment to death was 13 days (IQR 5 – 31 days). Conclusions: In a single institution self-evaluation, most patients referred to outpatient specialty palliative care completed referral within thirty days. Within the context of this institution, relevant goals for quality improvement interventions include increasing palliative care referrals among surgical oncologists and shifting palliative care referrals earlier in the disease course and prior to need for inpatient hospital admission. Understanding the elements of the creation of our palliative care referral database and relevant data tracking may serve as a method for other institutions to target interventions that may improve uptake of outpatient palliative care referrals at cancer centers more broadly.

Isochrone Fitting of Galactic Globular Clusters—VI. High-latitude Clusters NGC 5024 (M53), NGC 5053, NGC 5272 (M3), NGC 5466, and NGC 7099 (M30)

We fit various color–magnitude diagrams (CMDs) of the high-latitude Galactic globular clusters NGC 5024 (M53), NGC 5053, NGC 5272 (M3), NGC 5466, and NGC 7099 (M30) by isochrones from the Dartmouth Stellar Evolution Database and Bag of Stellar Tracks and Isochrones for α–enrichment [α/Fe] = +0.4. For the CMDs, we use data sets from Hubble Space Telescope, Gaia, and other sources utilizing, at least, 25 photometric filters for each cluster. We obtain the following characteristics with their statistical uncertainties for NGC 5024, NGC 5053, NGC 5272, NGC 5466, and NGC 7099, respectively: metallicities [Fe/H] = −1.93 ± 0.02, −2.08 ± 0.03, −1.60 ± 0.02, −1.95 ± 0.02, and −2.07 ± 0.04 dex with their systematic uncertainty 0.1 dex; ages 13.00 ± 0.11, 12.70 ± 0.11, 11.63 ± 0.07, 12.15 ± 0.11, and 12.80 ± 0.17 Gyr with their systematic uncertainty 0.8 Gyr; distances (systematic uncertainty added) 18.22 ± 0.06 ± 0.60, 16.99 ± 0.06 ± 0.56, 10.08 ± 0.04 ± 0.33, 15.59 ±0.03 ± 0.51, and 8.29 ± 0.03 ± 0.27 kpc; reddenings E(B − V) = 0.023 ± 0.004, 0.017 ± 0.004, 0.023 ± 0.004, 0.023 ± 0.003, and 0.045 ± 0.002 mag with their systematic uncertainty 0.01 mag; extinctions A V = 0.08 ± 0.01, 0.06 ± 0.01, 0.08 ± 0.01, 0.08 ± 0.01, and 0.16 ± 0.01 mag with their systematic uncertainty 0.03 mag, which suggest the total Galactic extinction A V = 0.08 across the whole Galactic dust to extragalactic objects at the North Galactic Pole. The horizontal branch morphology difference of these clusters is explained by their different metallicity, age, mass-loss efficiency, and loss of low-mass members in the evolution of the core-collapse cluster NGC 7099 and loose clusters NGC 5053 and NGC 5466.

Does intervention engagement mediate physical activity change in a web-based computer-tailored physical activity intervention?-Secondary outcomes from a randomised controlled trial.

The relationship between intervention engagement and behaviour change may vary depending on the specific engagement metric being examined. To counter this composite engagement measures may provide a deeper understanding of the relationship between engagement and behaviour change, though few studies have applied such multidimensional engagement metrics. The aim of this secondary analysis of RCT data was to examine how a composite engagement score mediates the effect of a web-based computer-tailored physical activity intervention. 501 inactive Australian adults were randomised to a no-treatment control or intervention group. Intervention participants received 8 sessions of web-based personalised physical activity advice over a 12-week intervention period and the ability to complete action plans. Change in physical activity was assessed using Actigraph accelerometers at baseline, 3-months and 9-months. Engagement with the intervention (i.e., a composite score including frequency, intensity, duration and type) was continuously assessed during the intervention period using website tracking software and database metrics. Generalised structural equation models were used to examine how a composite engagement score mediated intervention effects at 3 months and 9 months. At 3 months, mediation analysis revealed that the intervention group had significantly higher engagement scores than the control group [a-path exp(b) = 6.462, 95% CI = 5.121-7.804, p < 0.001]. Further, increased engagement with the intervention platform was associated with an increased time spent in moderate-to-vigorous physical activity [ab-coefficient exp(b) = 1.008, 95% CI = 1.004-1.014, P < 0.001]; however, the magnitude of this effect was small. There were no significant mediation effects at the 9-month time point. The findings suggest that a composite intervention engagement score has a small positive influence on physical activity changes and that other factors (e.g., behaviour change techniques) are likely to be more important drivers of behaviour change.

Association Between Statin Use and the Incidence of Clinically Diagnosed Osteoarthritis: A Nationwide Retrospective Cohort Study in Taiwan.

To investigate the effect of higher cumulative defined daily dose per year (cDDD/y) compared with lower cDDD/y of statin use in the incidence of any joint osteoarthritis (OA). In this population-based retrospective cohort study, patients who were aged ≥40 years were newly initiated on statin therapy between 2002 and 2011, and had a statin prescription for ≥90 days in the first year of treatment were identified from the 2000 Longitudinal Generation Tracking Database. All patients were separated into groups with higher cDDD/y (>120 cDDD/y) and lower cDDD/y (≤120 cDDD/y; as an active comparator) values. Propensity score matching was performed to balance potential confounders. All recruited patients were followed up for 8 years. Marginal Cox proportional hazard models were used to estimate time-to-event outcomes of OA. Compared with lower cDDD/y use, higher cDDD/y use did not reduce the risk of any joint OA (adjusted hazard ratio, 1.07; 95% confidence interval, 0.99-1.14). Dose-related analysis did not reveal any dose-dependent association. A series of sensitivity analyses showed similar results. Joint-specific analyses revealed that statin did not reduce the incidence of knee, hand, hip, and weight-bearing (knee or hip) OA. Higher cDDD/y statin use did not reduce the risk of OA in this Taiwanese nationwide cohort study. The complexity of OA pathogenesis might contribute to the ineffectiveness of statin. Repurposing statin with its anti-inflammation properties might be ineffective for OA development, and balancing the catabolism and anabolism of cartilage might be a major strategy for OA prevention.

Use of antiviral drugs and incidence of Parkinson's disease in Taiwan.

Patients infected with herpes zoster might be at risk for Parkinson's disease (PD). However, antiviral drugs may impede viral deoxyribonucleic acid (DNA) synthesis. This study aimed to determine whether the currently observed association between herpes zoster and PD is consistent with previous findings, and whether antiviral drug use is associated with PD. This retrospective cohort study used the Longitudinal Generation Tracking Database. We included patients aged 40 years and above and applied propensity score matching at 1:1 ratio for study comparability. PD risk was evaluated using Cox proportional hazards regression methods. A total of 234,730 people were analyzed. The adjusted hazard ratio (aHR) for PD in patients with herpes zoster was 1.05. Furthermore, the overall incidence of PD was lower in those treated with antiviral drugs than in the untreated ones (3.17 vs. 3.76 per 1,000 person-years); the aHR was 0.84. After stratifying for sex or age, a similar result was observed. In conclusion, herpes zoster may increase the risk of PD, particularly among females, but receiving antiviral treatment reduces the risk by 16%. Therefore, using antiviral drugs may help prevent PD. However, additional research is required to determine the underlying mechanism(s).

Association of Cardiopulmonary Hemodynamics and Mortality in Veterans With Liver Cirrhosis: A Retrospective Cohort Study.

Portopulmonary hypertension (PoPH), associated with increased mortality, can limit treatment options for liver diseases. Data on the continuum of clinical risk related to cardiopulmonary hemodynamics in PoPH are lacking. As part of the United States national Veterans Affairs Clinical Assessment, Reporting, and Tracking database, we performed a retrospective cohort study of adults with cirrhosis undergoing right heart catheterization between October 1, 2017, and September 30, 2022. Pulmonary hypertension (mean pulmonary arterial pressure [mPAP] >20 mm Hg without PoPH) and PoPH (mPAP >20 mm Hg+pulmonary artery wedge pressure ≤15 mm Hg+pulmonary vascular resistance ≥3 WU) were defined by right heart catheterization hemodynamics. Multivariable Cox proportional hazards using natural splines for hemodynamic variables were used to evaluate the association between cardiopulmonary hemodynamics and mortality following right heart catheterization. A total of 4409 patients were included in the final analysis, predominantly men (96.3%), with a mean age of 68.5 years. Pulmonary hypertension and PoPH were observed in 71.6% and 10.2% of the cohort, respectively. Compared with a reference cardiac index of 2.5 L/min per m2, the hazard for mortality increased progressively with decreasing cardiac index, even after adjustment for mPAP and pulmonary vascular resistance. The minority of patients with PoPH (N=65, 14.5%) were prescribed pulmonary vasodilator therapy. These data suggest that pulmonary hypertension and PoPH are prevalent in veterans with chronic liver disease, but low use of targeted PoPH therapy persists. Cardiac function discriminated mortality risk across a wide range of mPAP and pulmonary vascular resistance values and may diagnose and clarify prognosis in this patient population.

Improvement of Data Operations Management using CMMI and DMBOK in Soekarno-Hatta Meteorology Station

Soekarno-Hatta Meteorology Station is a Technical Implementation Unit within Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). It has the duties to manage, and process data for safety, regulation, and aviation navigation efficiency by distributing the processed data to their stakeholder, such as Air Traffic Service (ATS), flight operators, and pilots. Furthermore, research was also conducted in the station regarding the creation of Aerodrome Climatological Summary (ACS) annually. Loss of flight document data occurred in 2019 due to a ransomware attack, causing the cessation of the data operation process on the flight document. Unfortunately, there is no data recovery resulting in the unavailability of flight document data. This accident is very crucial because flight document data should not be lost within a 30-day timeframe as it is needed in case of any flight accident mechanism. It shows that Data Operations Management needs to be improved to support the business process. This study aims to evaluate the maturity level of data operations management in Soekarno-Hatta Meteorology Station by a conceptual model based on DAMA-DMBOK and the level of maturity is assessed using the Capability Maturity Model Integration (CMMI). The result showed that the average activity reaches level 2. Six activities are at level 1, namely obtaining externally sourced data, plan for data recovery, set database performance service levels, archive, retain, and purge data, support specialized database inventory and track data technology licenses, support data technology usage and issues. The rest of the activities will require action to improve their maturity level as given in the recommendation.

ChatGPT for Automated Cross-Checking of Authors' Conflicts of Interest Against Industry Payments.

The Centers for Medicare & Medicaid Services "OpenPayments" database tracks industry payments to US physicians to improve research conflicts of interest (COIs) transparency, but manual cross-checking of articles' authors against this database is labor-intensive. This study aims to assess the potential of large language models (LLMs) like ChatGPT to automate COI data analysis in medical publications. An observational study analyzing the accuracy of ChatGPT in automating the cross-checking of COI disclosures in medical research articles against the OpenPayments database. Publications regarding Food and Drug Administration-approved biologics for chronic rhinosinusitis with nasal polyposis: omalizumab, mepolizumab, and dupilumab. First, ChatGPT evaluated author affiliations from PubMed to identify those based in the United States. Second, for author names matching 1 or multiple payment recipients in OpenPayments, ChatGPT undertook a comparative analysis between author affiliation and OpenPayments recipient metadata. Third, ChatGPT scrutinized full article COI statements, producing an intricate matrix of disclosures for each author against each relevant company (Sanofi, Regeneron, Genentech, Novartis, and GlaxoSmithKline). A random subset of responses was manually checked for accuracy. In total, 78 relevant articles and 294 unique US authors were included, leading to 980 LLM queries. Manual verification showed accuracies of 100% (200/200; 95% confidence interval [CI]: 98.1%-100%) for country analysis, 97.4% (113/116; 95% CI: 92.7%-99.1%) for matching author affiliations with OpenPayments metadata, and 99.2% (1091/1100; 95% CI: 98.5%-99.6%) for COI statement data extraction. LLMs have robust potential to automate author-company-specific COI cross-checking against the OpenPayments database. Our findings pave the way for streamlined, efficient, and accurate COI assessment that could be widely employed across medical research.