High-risk Clusters Research Articles

Abstract 4138701: Phenotypic clustering of HFpEF using cardiac magnetic resonance imaging

Background: Improved understanding of the high-risk features associated with HFpEF may facilitate risk stratification. Cardiac magnetic resonance (CMR)-based strain and structural data can define detailed myocardial structure, which can be leveraged to define heterogenous HFpEF phenotypes. This study used phenotypic clustering integrating CMR-based strain and structural data to identify a high-risk HFpEF phenotype. Methods: Retrospective analyses of 48 HFpEF participants, who underwent cardiac MRI and invasive cardiopulmonary exercise testing (median time between the two studies=40 days), was performed. We conducted unsupervised and unbiased K-means clustering analyses using CMR-based strain and myocardial structural variables. We associated two identified clusters with mortality using Cox proportional hazards modeling and constructed Kaplan-Meier survival curves. A p-value was considered significant if < 0.05. Results: We identified a low (n=32) and high-risk (n=16) cluster. Patients in the identified clusters had similar ages and comorbidities. The high-risk cluster had more male participants and hemodynamically had a higher exercise mPAP/CO and PAWP/CO slope, which are metrics of pulmonary vascular disease. The high-risk cluster displayed greater LV mass, worse biventricular longitudinal strain, decreased right ventricular ejection fraction, and greater interventricular septal angle (all P<0.05) as characterized by CMR as compared to the low-risk cluster (Table 1, Figure 1A). Mortality for the high-risk cluster was 5.43 times that for the low-risk cluster at 12 months (95% CI 1.69-17.42, Figure 1B). Conclusions: Using clustering analyses with CMR strain and structural variables, we identified a high-risk HFpEF phenotype with increased LV mass, reduced biventricular strain, and reduced RV ejection fraction.

Dynamics trajectory of patient-reported quality of life and its associated risk factors among hepatocellular carcinoma patients receiving immune checkpoint inhibitors: a prospective cohort study

ObjectiveWe aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy.MethodsBarcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models. Associations with trajectory group membership were analyzed using multivariable multinomial logistic regression.ResultsThree trajectory groups were identified (n=156): excellent (35.3%), poor (43.6%), and deteriorating (21.1%) QOL. The deteriorating trajectory group reported a mean QOL score of 124.79 (95% CI, 116.58–133.00), but then declined significantly at month-2 (estimated QOL score 98.67 [95% CI, 84.33–113.00]), and the lowest mean score is reached at month-6 (estimated QOL score 16.58 [95% CI, 0–46.07]). Factors associated with membership to the deteriorating group included no drinking (odds ratio [OR] vs yes [95% CI], 3.70 [1.28–11.11]), no received radiotherapy (OR vs yes [95% CI], 8.33 [1.41–50.00]), diabetes (OR vs no [95% CI], 6.83 [1.57–29.73]), and extrahepatic metastasis (OR vs no [95% CI], 3.08 [1.07–8.87]). Factors associated with membership to the poor group also included body mass index ≤24.0 kg/m2 (OR vs no [95% CI], 4.49 [1.65–12.22]).ConclusionsThis latent-class analysis identified a high-risk cluster of patients with severe, persistent post-immunotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of immunotherapy and preserve QOL.

Machine-learning phenotyping of patients with functional mitral regurgitation undergoing transcatheter edge-to-edge repair - the MITRA-AI study

Abstract Background Severe functional mitral regurgitation (FMR) may benefit from transcatheter mitral valve repair (TMVR), but selection of patients remains to be optimised. Objectives The aim of this study was to use Machine-Learning (ML) approaches to uncover concealed connections between clinical, echocardiographic, and hemodynamic data associated with patients' outcomes. Methods Consecutive patients undergoing TMVR from 2009 to 2020 were included in the MITRA-AI registry. Eleven relevant clinical and echocardiographic variables were selected in a clustering-based model. The primary endpoint was a composite of cardiovascular death or heart failure hospitalisation at one year, while its single components were secondary endpoints. External validation was performed on the Mitrascore dataset and comparison between clustering and Mitrascore was performed with Net Reclassification Index (NRI). Moreover cluster assignment was performed also on a cohort of patients with moderate-to-severe MR treated with medical therapy alone. Results 822 patients were included in the derivation cohort. The composite primary endpoint occurred in 250 (30%) patients. Four clusters with decreasing risk of the primary endpoint were identified (42%, 37%, 25% and 20% from cluster 1 to cluster 4, respectively). Clusters were combined into a high-risk (clusters 1 and 2) and a low-risk phenotype (clusters 3 and 4). High-risk phenotype patients had larger LVs (> 107 ml/m2), lower LVEF (< 35%) and more prevalent ischemic aetiology (51% to 60% vs. 30% to 40%) compared to low-risk phenotype patients. Moreover, within the high-risk group, patients with diabetes mellitus and with advanced age were at increased risk. Within the low-risk group, ischemic aetiology increased the risk of cardiovascular death, while permanent atrial fibrillation amplified that of HF hospitalizations. In the Mitrascore validation cohort of 1119 patients, incidence of the primary end point varied consistently (48%, 52%, 35% and 42%). Clustering achieved a NRI in 27% of the patients in the derivation cohort and 7% in the validation group compared to Mitrascore. In the validation group of 207 patients treated medically, the incidence of the primary endpoint decreased from the first to the fourth cluster (53%, 42%, 30% and 30%). Conclusions A ML analysis identified meaningful clinical phenotypic presentations in FMR undergoing TMVR, with significant differences in terms of cardiovascular death and heart failure hospitalizations, confirmed in an external validation cohort. ML phenotyping also discriminated prognosis of medically treated patients with severe MR.Central Figure

Ecuador Towards Zero Leprosy: A Twenty-Three-Year Retrospective Epidemiologic and Spatiotemporal Analysis of Leprosy in Ecuador.

Ecuador has gone through a significant reduction in new cases from 2000 (106) to 2023 (12), suggesting a trend towards zero leprosy. An ecological spatiotemporal study design was used to describe the epidemiological distribution of the disease in the country during 2000-2023. Leprosy cases registered by the surveillance system of the Ecuadorian Ministry of Public Health were the data utilized for the study. From January 2000 to December 2023, 1539, incidence cases were diagnosed with leprosy in Ecuador. At the time of diagnosis, the median age was 54 years. Most of the cases were males (71.5%). The proportion of incidence cases in subjects over 50 years was 63% and 1.5% in children ≤ 15 years old. The yearly incidence rate ranged from 8.5/1,000,000 population in 2000 to 0.68/1,000,000 population in 2023, remaining within the low-endemic parameter. In total, 35 cantons reported newly detected leprosy cases in the year 2000. By the end of 2023, only eight cantons actively reported cases of leprosy. High-risk clusters for leprosy were detected in the tropical coastal region of Ecuador. The provinces with the highest number of cases during the study period were Guayas (44.8%) and Los Rios (15.7%), with zero cases being found in the Galapagos Islands. Our study is unique in that it documents a retrospective dataset over a two-decade timespan from a South American country that has effectively applied global guidelines for the control and elimination of leprosy.

Application of spatiotemporal scan statistics in cases of intentional homicides in northern Brazil

The crime of intentional homicide in Brazil is worrying. In the northern region, this type of crime has been growing since 2020. In this sense, we decided to apply Kuldorff's prospective space-time scan statistics in order to identify emerging counties. We conclude that there are two emerging clusters of high relative risk (Amazonas and Pará) that require rapid intervention and two clusters of low relative risk (Acre, Roraima, Amazonas, Amapá and Pará) that do not require urgent intervention. Some characteristics of these two clusters are presented: radius, population, relative risk, likelihood ratio.

Air pollution’s numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

BackgroundWhile stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.MethodsMonthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009–2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors’ numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).ResultsOur findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse ‘V’-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5’s impact peaked in autumn and was weakest in spring, whereas O3’s effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.ConclusionsThis study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.

Clustering-based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks.

This study aimed to identify low- and high-risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering-based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions. Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow-up data. K-means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method. We identified two stable clusters representing high- and low-risk diabetes groups in both biobanks. The high-risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low-risk clusters, respectively. Notably, males were predominant in the high-risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high-risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high-risk group (P < 0.001). Kaplan-Meier curves indicated significant differences in diabetes complication incidences between clusters. UL effectively identified risk-specific groups within prediabetes populations, with high-risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk.

Green spaces and flood vulnerability in urbanising regions: a case study in the Philadelphia metropolitan area

ABSTRACT Flooding continues to be one of the most significant natural hazards, disproportionately impacting vulnerable populations in urbanising regions. This study aims to examine the relationship between green space density and flood vulnerability in the Philadelphia Metropolitan Area by integrating social and geophysical data. The research focuses on identifying areas where high flood vulnerability coincides with low green space availability, as these are critical regions for targeted intervention. Using social vulnerability indicators such as income, age, and housing characteristics, combined with geophysical factors like soil permeability and drainage density, the study assesses flood vulnerability across census block groups. Local Indicators of Spatial Association (LISA) and discriminant analysis were employed to detect spatial clusters with elevated flood risks and low green space density. Results reveal that these high-risk clusters are concentrated in densely urbanised areas such as Norristown, where lower-income and elderly populations are overrepresented. These findings reinforce broader environmental justice concerns, as socially vulnerable communities face compounded risks from both inadequate flood protection and limited access to green spaces. The study emphasises the need for integrated flood management, combining green infrastructure with structural interventions, to guide urban planners and policymakers in reducing flood risks and promoting environmental equity.

High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level

BackgroundDespite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022.MethodsGeographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran’s I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters.ResultsBetween 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013.ConclusionsIn this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

K-Means and Fuzzy C-Means Cluster Food Nutrients for Innovative Diabetes Risk Assessment

Packaged food and beverages often pose a risk of increasing diabetes when consumed regularly. This study aims to classify these products based on their nutritional content listed on the labels, with a focus on identifying diabetes risk. The methods employed include K-Means and Fuzzy C-Means, K-Means is used to determine initial center of cluster, while Fuzzy C-Means enhances the clustering by assigning probabilistic memberships to each data point. These methods are applied to products sold in stores in Banyumas Regency, Central Java, Indonesia. This research is the first to combine these two methods in the context of product clustering based on nutritional labels. The results indicate that packaged food and beverage products can be classified into high-risk and low-risk clusters for diabetes. Consequently, this study provides important guidance for consumers in choosing healthier.

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency

Background & objectivesMental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted interventions. This study aims to develop a risk assessment tool for anxiety, depression, and self-perceived stress using machine learning (ML) and explainable AI to identify key risk factors and stratify the population into meaningful risk profiles. MethodsWe utilized a cohort of 9291 individuals from Northern Spain, with extensive post-COVID-19 mental health surveys. ML classification algorithms predicted depression, anxiety, and self-reported stress in three classes: healthy, mild, and severe outcomes. A novel combination of SHAP (SHapley Additive exPlanations) and UMAP (Uniform Manifold Approximation and Projection) was employed to interpret model predictions and facilitate the identification of high-risk phenotypic clusters. ResultsThe mean macro-averaged one-vs-one AUROC was 0.77 (± 0.01) for depression, 0.72 (± 0.01) for anxiety, and 0.73 (± 0.02) for self-perceived stress. Key risk factors included poor self-reported health, chronic mental health conditions, and poor social support. High-risk profiles, such as women with reduced sleep hours, were identified for self-perceived stress. Binary classification of healthy vs. at-risk classes yielded F1-Scores over 0.70. ConclusionsCombining SHAP with UMAP for risk profile stratification offers valuable insights for developing effective interventions and shaping public health policies. This data-driven approach to mental health preparedness, when validated in real-world scenarios, can significantly address the mental health impact of public health crises like COVID-19.

Changes in 10-Year Predicted Cardiovascular Disease Risk for a Multiethnic Semirural Population in South East Asia: Prospective Study.

Cardiovascular disease (CVD) risk factors tend to cluster and interact multiplicatively and have been incorporated into risk equations such as the Framingham risk score, which can reasonably predict CVD over short- and long-term periods. Beyond risk factor levels at a single time point, recent evidence demonstrated that risk trajectories are differentially related to CVD risk. However, factors associated with suboptimal control or unstable CVD risk trajectories are not yet established. This study aims to examine factors associated with CVD risk trajectories in a semirural, multiethnic community-dwelling population. Data on demographic, socioeconomic, lifestyle, mental health, and cardiovascular factors were measured at baseline (2013) and during follow-up (2018) of the South East Asia Community Observatory cohort. The 10-year CVD risk change transition was computed. The trajectory patterns identified were improved; remained unchanged in low, moderate, or high CVD risk clusters; and worsened CVD risk trajectories. Multivariable regression analyses were used to examine the association between risk factors and changes in Framingham risk score and predicted CVD risk trajectory patterns with adjustments for concurrent risk factors. Of the 6599 multiethnic community-dwelling individuals (n=3954, 59.92% female participants and n=2645, 40.08% male participants; mean age 55.3, SD 10.6 years), CVD risk increased over time in 33.37% (n=2202) of the sample population, while 24.38% (n=1609 remained in the high-risk trajectory pattern, which was reflected by the increased prevalence of all major CVD risk factors over the 5-year follow-up. Meanwhile, sex-specific prevalence data indicate that 21.44% (n=567) of male and 41.35% (n=1635) of female participants experienced an increase in CVD risk. However, a stark sex difference was observed in those remaining in the high CVD risk cluster, with 45.1% (n=1193) male participants and 10.52% (n=416) female participants. Regarding specific CVD risk factors, male participants exhibited a higher percentage increase in the prevalence of hypertension, antihypertensive medication use, smoking, and obesity, while female participants showed a higher prevalence of diabetes. Further regression analyses identified that Malay compared to Chinese (P<.001) and Indian (P=.04) ethnicity, nonmarried status (P<.001), full-time employment (P<.001), and depressive symptoms (P=.04) were all significantly associated with increased CVD risk scores. In addition, lower educational levels and frequently having meals from outside were significantly associated to higher odds of both worsening and remaining in high CVD risk trajectories. Sociodemographics and mental health were found to be differently associated with CVD risk trajectories, warranting future research to disentangle the role of psychosocial disparities in CVD. Our findings carry public health implications, suggesting that the rise in major risk factors along with psychosocial disparities could potentially elevate CVD risk among individuals in underserved settings. More prevention efforts that continuously monitor CVD risk and consider changes in risk factors among vulnerable populations should be emphasized.

Street-level built environment on SARS-CoV-2 transmission: A study of Hong Kong

Understanding the association between SARS-CoV-2 Spatial Transmission Risk (SSTR) and Built Environments (BE) is crucial for implementing effective pandemic prevention measures. Massive efforts have been made to examine the macro-built environment at the regional level, which has neglected the living service areas at the residential scale. Therefore, this study aims to explore the association between Street-level Built Environments (SLBE) and SSTR in Hong Kong from the 1st to the early 5th waves of the pandemic to address this gap. A total of 3693 visited/resided buildings were collected and clustered by spatial autocorrelation, and then Google Street View (GSV) was employed to obtain SLBE features around the buildings. Eventually, the interpretable machine learning framework based on the random forest algorithm (RFA)-based SHapley Additive exPlanations (SHAP) model was proposed to reveal the hidden non-linear association between SSTR and SLBE.The results indicated that in the high-risk cluster area, street sidewalks, street sanitation facilities, and artificial structures were the primary risk factors positively associated with SSTR, in low-risk cluster areas with a significant positive association with traffic control facilities. Our study elucidates the role of SLBE in COVID-19 transmission, facilitates strategic resource allocation, and guides the optimization of outdoor behavior during pandemics for urban policymakers.

Unsupervised Machine Learning to Identify Risk Factors of Pyeloplasty Failure in Ureteropelvic Junction Obstruction.

Introduction: In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting pyeloplasty outcome, and there is no unified definition of pyeloplasty success. As such, defining pyeloplasty success retrospectively is particularly vulnerable to bias, allowing researchers to choose significant outcomes with the benefit of hindsight. To mitigate these biases, we performed an unsupervised machine learning cluster analysis on a dataset of 216 pyeloplasty patients between 2015 and 2023 from a multihospital system to identify the defining risk factors of patients that experience worse outcomes. Methods: A KPrototypes model was fitted with pre- and perioperative data and blinded to postoperative outcomes. T-test and chi-square tests were performed to look at significant differences of characteristics between clusters. SHapley Additive exPlanation values were calculated from a random forest classifier to determine the most predictive features of cluster membership. A logistic regression model identified which of the most predictive variables remained significant after adjusting for confounding effects. Results: Two distinct clusters were identified. One cluster (denoted as "high-risk") contained 111 (51.4%) patients and was identified by having more comorbidities, such as old age (62.7 vs 35.7), high body mass index (BMI) (26.9 vs 23.8), hypertension (66.7% vs 17.1%), and previous abdominal surgery (72.1% vs 37.1%) and was found to have worse outcomes, such as more frequent severe postoperative complications (7.2% vs 1.0%). After adjusting for confounding effects, the most predictive features of high-risk cluster membership were old age, low preoperative estimated glomerular filtration rate (eGFR), hypertension, greater BMI, previous abdominal surgery, and left-sided UPJO. Conclusions: Adult UPJO patients with older age, lower eGFR, hypertension, greater BMI, previous abdominal surgery, and left-sided UPJO naturally cluster into to a group that more commonly suffers from perioperative complications and worse outcomes. Preoperative counseling and perioperative management for patients with these risk factors may need to be thought of or approached differently.

Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan.

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks.

Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes

The immunophenoscore (IPS) is an important indicator for evaluating immunotherapy response. This work was designed to establish a prognostic model based on IPS-related genes in cervical cancer. Weighted correlation network analysis (WGCNA) was utilized to identify key modules related to IPS in cervical cancer data from The Cancer Genome Atlas (TCGA). The results show that the yellow module (158 genes) had a high correlation with both IPS_CTLA4_blocker and IPS_CTLA4_and PC1/PDL1/PDL2 blocker. Univariate cox regression analysis and LASSO regression analysis were performed based on 158 genes, and 9 characteristic genes were finally identified to construct the model. According to the differentially expressed genes, cervical cancer samples were divided into high-risk and low-risk groups and cluster 1.2.3. Higher risk scores associated with poorer prognosis. cluster2 and cluster3 were identified as cervical cancer subtypes with significant survival differences. cluster2 had higher immune cell infiltration levels and better prognosis, with greater sensitivity to Cyclopamine, Imatinib, MG-13, Paclitaxel, PHA-665752, Rapamycin, Sorafenib, Sunitinib, and VX-680. In contrast, cluster3 had higher TTN and PIK3CA mutations and greater sensitivity to AZ628, Dasatinib, Doxorubicin, HG-6-64-1, JQ12, Midostaurin, PF-562271, TAE684, and WH-4-023. In conclusion, we developed a feasible risk score model based on IPS-related genes for cervical cancer prognosis and identified potential drugs for different cervical cancer subtypes.

Bayesian spatio-temporal analysis of dengue transmission in Lao PDR

Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People’s Democratic Republic (Lao PDR). This study aimed to describe spatial–temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR.

The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation.

Cardiovascular Disease (CVD) is currently the major challenge to people's health and the world's top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.

Spatiotemporal distribution and risk factors for patient and diagnostic delays among groups with tuberculous pleurisy: an analysis of 5-year surveillance data in eastern China.

To understand and analyze the factors relating to patient and diagnostic delays among groups with tuberculous pleurisy (TP), and its spatiotemporal distribution in Zhejiang Province. Data of all tuberculous pleurisy patients were collected from the existing Tuberculosis Information Management System. A time interval of > 2 weeks between first symptom onset and visit to the designated hospital was considered a patient delay, and a time interval of > 2 weeks between the first visit and a confirmed TP diagnosis was considered a diagnostic delay. Univariate and multivariate logistic regression analyses were used to explore factors influencing patient and diagnostic delays in patients with TP. Spatial autocorrelation and spatiotemporal scan analyses were used to identify hot spots and risk clusters, respectively. In total, 10,044 patients with TP were included. The median time and interquartile range for patients seeking medical care and diagnosis were 15 (7-30) and 1 (0-8) days, respectively. The results showed that people aged > 65 years, retirees, and residents of Jinhua, Lishui, and Quzhou were positively correlated with patient delay, whereas retreatment patients, houseworkers, unemployed people, and residents of Zhoushan or Ningbo were positively correlated with diagnostic delay. Additionally, high-risk clusters of patient delays were observed in the midwestern Zhejiang Province. The most likely clusters of TP diagnostic delays were found in southeast Zhejiang Province. In summary, patient delay of TP in Zhejiang province was shorter than for pulmonary tuberculosis in China, while the diagnostic delay had no difference. Age, city, occupation, and treatment history were related to both patient and diagnostic delays in TP. Interventions in central and western regions of Zhejiang Province should be initiated to improve the early detection of TP. Additionally, the allocation of health resources and accessibility of health services should be improved in the central and eastern regions of Zhejiang Province.

Prevalence, spatial distribution and risk mapping of Dirofilaria immitis in wild canids in southern Québec, Canada

Domestic dogs (Canis familiaris) and wild canids, including coyotes (Canis latrans) and red foxes (Vulpes vulpes), serve as definitive hosts for Dirofilaria immitis, a parasitic nematode causing the heartworm disease. Understanding infection risks in wildlife reservoirs in relation to environmental factors is crucial for assessing exposure risk in domestic dogs. The regional prevalence of D. immitis infection was estimated in trapped wild coyotes and red foxes across Québec, Canada. Spatial clusters of infection were detected using Kulldorff's spatial scan statistics. A series of logistic regression models predicting the D. immitis status in coyotes were built from heartworm development unit (HDU) estimates and cumulative precipitation variables over various time periods. Between October 2020 and March 2021, 421 coyotes and 284 red foxes were examined for the presence of D. immitis. The parasite was found in 43 coyotes and 1 red fox. A high-risk infection cluster was detected in coyotes in southwestern Québec. The best model included as sole predictor the average cumulative HDU contributing to risk of D. immitis in the three years preceding coyote capture. This model significantly predicted infection status with an area under the curve of 76.1%. The cumulative precipitation had no notable effect in any model. This study highlights a high prevalence of D. immitis in coyotes in Québec with regional differences correlated to temperature-derived predictors. The spatial risk of infection in this population likely represents the environmental risk of exposure to the parasite given that coyotes do not receive preventive treatment compared to domestic dogs. Our findings are important for veterinarians in the application of prevention strategies for heartworm disease in domestic dogs.