Independent Populations Research Articles

Diagnostic stewardship and dermatology consultation in cellulitis management: a systematic literature review and meta-analysis.

Cellulitis is a common skin infection often requiring antibiotic treatment. However, misdiagnosis and inappropriate antibiotic use contribute to antibiotic resistance and healthcare costs. We aimed to evaluate the impact of dermatology consultation on treatment modification in patients with suspected cellulitis and to determine whether dermatologists' evaluation can be used as a reference to diagnose suspected cellulitis. We explored MedLine (PubMed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane CENTRAL, Web of Science, and Scopus and Embase, including publications from database inception to July 25, 2023. Studies were included if they evaluated treatment modifications involving the use of antibiotics for patients with suspected cellulitis with and without dermatology consultation. We excluded comments or reviews, pilot studies, and studies without a non-dermatology control group, treatment modifications, the use of antibiotics, or patients with cellulitis. Of the 49 full-text articles, 14 studies met the selection criteria.This systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Of five independent reviewers (GYC, IP, MG, MKH, and AMRP), two abstracted data for each article using a standardized abstraction form. We used the Downs and Black scale to evaluate study quality. Subgroup analysis was conducted regarding readmission rate within 30 days for two independent populations seen by a non-dermatologist physician or a dermatologist. We employed random-effects models to obtain pooled mean differences. Heterogeneity was assessed using the I-squared value. The impact of dermatology consultation on treatment modification involving antibiotics in patients with suspected cellulitis and readmission rates in 30 days. Dermatology consultation changed initial treatment plans involving antibiotics from 47 to 96% of the time, improving diagnostic accuracy and, consequently, antibiotic stewardship of cellulitis. Dermatology consultation was not significantly associated with lower readmission rates in 30 days (pooled OR = 0.56, 95% CI: 0.25 to 1.25, I2 = 0%). Dermatology consultation in patients with suspected cellulitis may improve diagnosis and management, thereby reducing antibiotic misuse, unnecessary tests, and prolonged hospitalizations.

Detection of Increased Activity of Human Parvovirus B19 Using Commercial Laboratory Testing of Clinical Samples and Source Plasma Donor Pools - United States, 2024.

In most persons, human parvovirus B19 (B19) causes a mild respiratory illness, but infection can result in adverse health outcomes in persons who are pregnant, immunocompromised, or who have chronic hemolytic blood disorders. During the first quarter of 2024, several European countries reported increases in B19 activity. In the United States, there is no routine surveillance for B19. To assess increases in B19 activity in the United States, trends in testing and results from two independent populations were examined: 1) the presence of immunoglobulin (Ig) M antibodies, a marker of recent infection, in clinical specimens ordered by physicians and 2) B19 nucleic acid amplification testing (NAAT) in pooled donor source plasma from a large commercial laboratory during 2018-2024. The proportion of IgM-positive clinical specimens reached 9.9% in the second quarter (Q2) of 2024 after remaining <1.5% during 2020-2023 and was higher than Q2 peaks in 2018 (3.8%, p<0.001) and 2019 (5.1%, p<0.001). The prevalence of B19-NAAT-positive donor pools (512 donations per pool) reached 20% in June 2024 after remaining <2% during 2020-2023 and was higher than peaks in 2018 (6.7%, p<0.001) and 2019 (7.3%, p<0.001). Considering the B19 activity increase in the United States in 2024, promotion of measures to prevent respiratory viruses and monitor for adverse B19-related outcomes by health care providers and public health authorities might reduce adverse health outcomes in pregnant persons and others at increased risk.

Effects of Anatomical Variation on Ganglion Cell and Nerve Fibre Layer Evaluation by Optical Coherence Tomography

Background/Objectives: The automated analyses of optical coherence tomography (OCT) scans of the retina occasionally suggest the presence of tissue deficits when no visual field defects can be detected. This study was made to find the sources of such alerts. Methods: Data from a population-based cohort of 360 participants aged 30–80 years was analysed for the anatomical sources of alerts after the extensive exclusion of participants where any suspicion of abnormality could be raised. An analysis was made of 12 × 9 mm volume scans centred between the disc and the fovea. The exclusions comprised 107 eyes with definite or borderline abnormal visual fields or other potentially confounding characteristics. A statistical analysis of the thickness patterns was made using the manufacturer’s proprietary algorithm. The analysis comprised alerts corresponding to local layer thickness values in the lower 5th percentile of an independent reference population. Results: Of the 613 eligible healthy eyes, thickness deficit alerts were seen in 391. They were related to the angle between the temporal nerve fibre ridges being wider, narrower, or rotated compared to the reference template in 174 eyes and to the variations in the size of the macula in 207 eyes. The source was unidentifiable in 28 eyes. The common sources were a thin papillomacular nerve fibre layer accompanied by arcuate nerve fibre ridges spaced far apart and a thinly, but wider than the normal macular ganglion cell layer. Conclusions: Anatomical variation in the retinal nerve fibre and ganglion cell layers was the source of more than 90% of the thickness deficit alerts in the eyes with normal visual fields.

About Whom Are We Talking When We Use Intellectual and Developmental Disabilities?

Communicating clearly about who is included in a population group is a critical element to effective dissemination and knowledge transfer. This narrative review highlights the inconsistency as it relates to communicating about individuals with intellectual disability (ID) and developmental disability (DD). There is enormous variability in the use of definitions and abbreviations in the field of intellectual disability and developmental disabilities. The lack of consistency has resulted in widely varying reported rates of prevalence and has contributed to confusion around the interpretation of research and clinical findings, population statistics, and policy decisions. The reported rates of prevalence for developmental disabilities published by different US federal agencies have ranged widely from 3% to 17%. This represents a 5-fold difference. Equally confusing is the inconsistent and ambiguous adoption of initialisms. These initialisms include ID/DD, IDD, and I/DD for which it is not always clear if these initialisms reference separate and independent populations (eg, ID or DD) or populations with co-occurring conditions (eg, persons with ID and DD). This Narrative Review discusses these issues and proposed a number of recommendations that would contribute to enhanced consistency and clarity of understanding for stakeholders, administrators, practitioners, researchers, and policy makers. Authors of scholarly works, clinical publications, policies, and position papers are encouraged to provide a clear operational definition as well as the choice of initialisms used (eg, ID/DD, IDD, I/DD, etc) when it is first used.

Assessment of Early Vascular Aging Ambulatory Score (EVAAs): A Large Population-based External Validation Study.

Pulse Wave Velocity (PWV) remains the gold-standard method to assess Early Vascular Aging (EVA) defined by arterial stiffness. However, its high cost, time-consuming process, and need for qualified medical staff shows the importance of identifying alternative methods for the EVA evaluation. In order to simplify the process of assessing patient's EVA, we recently developed the Early Vascular Aging Ambulatory score (EVAAs), a simple tool to predict the risk of EVA. The aim of the present study was the external validation of EVAAs in an independent population. Eight hundred seventy-nine (46.3% men) patients who were referred to our Hypertension ESH Excellence Center were included in this study. The mean age was 46.43 ± 22.87 years. EVA was evaluated in two different ways. The first assessment included c-f PWV values, whereas the second one included EVAAs without the direct measurement of carotid-femoral PWV. The null hypothesis was that the prediction of EVA based on EVAAs does not present any statistically significant difference compared to the prediction based on the calculation from c-f PWV. Mean squared error (MSE) was used for the assessment of the null hypothesis, which was found to be 0.40. The results revealed that the EVAAs shows the probability of EVA with 0.98 sensitivity and 0.75 specificity. The EVAAs present 95% positive predictive value and 92% negative predictive value. Our study revealed that EVAAs could be as reliable as the carotid-femoral PWV to identify patients with EVA. Hence, we hope that EVAAs will be a useful tool in clinical practice.

Longitudinal stability of molecular endotypes of knee osteoarthritis patients

ObjectiveTo assess the longitudinal stability of biomarker-based molecular endotypes of knee osteoarthritis (KOA) participants from APPROACH and to evaluate the consistency of findings in an independent KOA population. MethodsNineteen biomarkers were measured longitudinally in 295 KOA participants from the APPROACH cohort. K-means clustering was used to identify the structural damage, inflammation, and low tissue turnover endotypes at the six-, 12-, and 24-month follow-ups. Endotype stability was defined as having the same independent endotype assignment longitudinally for patients with complete data (n = 226). Clinical and biochemical characteristics were compared between participants with longitudinally stable and unstable endotypes. The presence and longitudinal stability of the endotypes were evaluated in a different KOA population from the placebo arm of the oral salmon calcitonin trials. ResultsAn average overall longitudinal endotype stability of 55% (Fleiss’ Kappa of 0.53; 95% CI: 0.46, 0.60) was demonstrated. An average stability of 59% (range: 54% - 59%) was observed for the structural damage endotype (Fleiss’ Kappa 0.52; 95% CI: 0.45, 0.60), 54% (52% - 56%) for the inflammatory (Fleiss’ Kappa 0.61; 95% CI: 0.53, 0.68), and 50% (49% - 52%) for the low tissue turnover endotype (Fleiss’ Kappa 0.46; 95% CI: 0.39, 0.54). Participants with longitudinally unstable endotypes exhibited molecular properties of more than one endotype, which were detectable already at the first visit. ConclusionsOur study showed for the first time that more than half of KOA participants exhibited a longitudinally stable endotype, highlighting the applicability of biomarker-based endotyping in a clinical trial setting.

Assessing the utility of the increased wall thickness score in diagnosing ATTR cardiomyopathy: a validation study

Abstract Introduction Cardiac amyloidosis due to transthyretin deposition (ATTR-CM) is often underdiagnosed. An increased left ventricular wall thickness (LVWT) should raise the suspicion for ATTR-CM, but it is a very frequent and non-specific finding. In order to improve diagnostic certainty, an echocardiographic score (Increased Wall Thickness [IWT] score) was recently proposed. The aim of our study was to assess the diagnostic performance of the IWT score in an independent population with increased LVWT. Methods We conducted a retrospective analysis of 295 consecutive patients studied at our centre for increased LVWT. All patients with severe aortic stenosis (AS) had ATTR-CM excluded by endomyocardial biopsy (EMB), during aortic valve replacement. ATTR-CM was considered as per the ESC algorithm. To be included, patients with Hypertrophic Cardiomyopathy (HCM) performed cardiac magnetic resonance and required a sarcomeric mutation for definite diagnosis. The echocardiographic IWT score was calculated for all patients, and categorized as certain (IWT≥8), unlikely (IWT ≤1) or indeterminate (1 &amp;lt; IWT &amp;lt; 8) for ATTR-CM diagnosis. Results A total of 295 patients were included (median age of 75 years [69-81], 61% men). Overall, 230 (70%) patients had hypertension and 164 (56%) chronic kidney disease (CKD). The median LVWT was 16 mm [IQR 15-19] and the median LVEF was 57% [IRQ 50-62%]. After diagnostic workup, 97 patients (33%) had confirmed ATTR-CM, 129 (44%) had isolated severe AS and 71 (24%) had hypertrophic cardiomyopathy (HCM). Fifteen patients had both severe AS and ATTR-CM. The median IWT score was 4 (IQR 2-7), with 21% of patients (n=64) showing an IWT score ≤1, 19% an IWT score ≥8, and 60% of patients (n=176) having an "indeterminate" value between 2 and 7. Despite this, on a continuous scale, the IWT showed good discriminative value (area under ROC curve 0.86, 95% 0.82-0.90, p&amp;lt;0.001). The criteria of IWT score ≥8 showed a positive predictive value of 82% (95% CI 72-89%), while an IWT score ≤ 1 yielded a negative predictive value of 92% (95% CI 84-97%) - Figure. Conclusions Even though the IWT score provides "indeterminate" results in a large proportion of patients, values at the ends of the spectrum showed good negative and positive predictive values for the diagnosis of ATTR-CM. Our findings provide external validation for this echocardiographic score, and support its use in the diagnostic workup of patients with increased wall thickness and suspected ATTR-CM.

Echocardiographic left atrial stiffness index predicts high left ventricular filling pressures in patients with acute heart failure: an observational study.

In acute heart failure (AHF) patients, non-invasive estimation of left ventricular filling pressures (LVFPs) appears crucial to guide management. Although poorly investigated, left atrial (LA) mechanics play a pivotal role in this setting. This report sought to assess the correlation of echocardiographic LA stiffness index with invasive LVFPs and its diagnostic accuracy as compared to other parameters used in clinical practice. In this observational, prospective study, 104 patients with suspected acute coronary syndrome and signs/symptoms of AHF were enrolled. Available invasive estimation of LVFPs was required. Comprehensive echocardiography was performed for all patients, including LA reservoir strain (LARS). LA stiffness index was derived by speckle-tracking analysis and Tissue Doppler imaging as early diastolic transmitral inflow velocity/mitral annulus early diastolic velocity [E/e']/LARS. Invasively measured LVFPs showed a strong correlation with LA stiffness index (Spearman ρ = 0.773, p < 0.0001), as well as with LARS and E/e'. Receiver operating characteristic (ROC) curve analysis was used to demonstrate better accuracy performed by LA stiffness index than average E/e', LA volume or LARS alone, in predicting high LVFPs. Guideline-recommended assessment of diastolic function was finally compared to LARS and LA stiffness index performances in an independent population group; we were hence able to obtain a LA stiffness threshold of 0.48 with a positive predictive value of 91.7% and a negative predictive value of 88.9% in identifying patients with high LVFPs. For the first time, diagnostic performance of LA stiffness index has been investigated in a heterogeneous AHF population, providing correlations with invasively measured LVFPs and comparisons with established diastolic function metrics.

Development and validation of the four-item Concise Aging adults Smell Test to screen for olfactory dysfunction in older adults.

Olfactory dysfunction (OD) in the elderly is common and associated with numerous comorbidities, yet often underrecognized. This study sought to develop an instrument for widespread screening of this condition. Demographic factors, comorbidities, and olfactory-related questions were examined in a focus group (N=20) and development cohort (N=190). The 4-item Concise Aging adults Smell Test (4-CAST) screening instrument was then developed to predict OD on Sniffin Sticks testing. An independent validation cohort (N=147) was then examined to confirm ability of the 4-CAST to predict OD on Smell Identification Test 40 (SIT40). The most important variables for predicting olfactory loss in older subjects were age, type II diabetes status, Visual Analog Scale (VAS) of overall rating of smell, and VAS of impact of smell upon safety. In the development cohort, the 4-CAST model yielded predictive probability (area under the curve) of 0.805 (p<0.001) for predicting the bottom 25th percentile on Sniffin Sticks. Results were similar in the validation cohort, as the 4-CAST accurately classified 83.8% subjects (area under the curve [AUC] 0.789, p<0.001) in the bottom 25th percentile of SIT40 testing. The 4-CAST was able to accurately classify 89.4% of subjects with anosmia. The 4-CAST is a quick screening instrument for predicting OD in older adults and has similar performance characteristics in independent populations using the two most common validated olfactory tests. Given the high prevalence of unrecognized olfactory loss in this population, this can be a useful tool for practitioners to determine which subjects may benefit from more extensive olfactory testing.

Inferring ancestry with the hierarchical soft clustering approach tangleGen.

Understanding the genetic ancestry of populations is central to numerous scientific and societal fields. It contributes to a better understanding of human evolutionary history, advances personalized medicine, aids in forensic identification, and allows individuals to connect to their genealogical roots. Existing methods, such as ADMIXTURE, have significantly improved our ability to infer ancestries. However, these methods typically work with a fixed number of independent ancestral populations. As a result, they provide insight into genetic admixture, but do not include a hierarchical interpretation. In particular, the intricate ancestral population structures remain difficult to unravel. Alternative methods with a consistent inheritance structure, such as hierarchical clustering, may offer benefits in terms of interpreting the inferred ancestries. Here, we present tangleGen, a soft clustering tool that transfers the hierarchical machine learning framework Tangles, which leverages graph theoretical concepts, to the field of population genetics. The hierarchical perspective of tangleGen on the composition and structure of populations improves the interpretability of the inferred ancestral relationships. Moreover, tangleGen adds a new layer of explainability, as it allows identifying the SNPs that are responsible for the clustering structure. We demonstrate the capabilities and benefits of tangleGen for the inference of ancestral relationships, using both simulated data and data from the 1000 Genomes Project.

Plasma acellular transcriptome contains Parkinson's disease signatures that can inform clinical diagnosis.

We aimed to identify plasma cell-free transcripts (cfRNA) associated with Parkinson's disease (PD) that also have a high predictive value to differentiate PD from healthy controls. Leveraging two independent populations from two different movement disorder centers we identified 2,188 differentially expressed cfRNAs after meta-analysis. The identified transcripts were enriched in PD relevant pathways, such as PD (p=9.26×10 -4 ), ubiquitin-mediated proteolysis (p=7.41×10 -5 ) and endocytosis (p=4.21×10 -6 ). Utilizing in-house and publicly available brain, whole blood, and acellular plasma transcriptomic and proteomic PD datasets, we found significant overlap across dysregulated biological species in the different tissues and the different biological layers. We developed three predictive models containing increasing number of transcripts that can distinguish PD from healthy control with an area under the ROC Curve (AUC) ≥0.85. Finally, we showed that several of the predictive transcripts significantly correlate with symptom severity measured by UPDRS-III. Overall, we have demonstrated that cfRNA contains pathological signatures and has the potential to be utilized as biomarker to aid in PD diagnostics and monitoring.

Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model

SummaryA multi-feature fusion DCNN model for automated evaluation of lumbar vertebrae L1 on chest combined with clinical information and radiomics permits estimation of volumetric bone mineral density for evaluation of osteoporosis.PurposeTo develop a multi-feature deep learning model based on chest CT, combined with clinical information and radiomics to explore the feasibility in screening for osteoporosis based on estimation of volumetric bone mineral density.MethodsThe chest CT images of 1048 health check subjects were retrospectively collected as the master dataset, and the images of 637 subjects obtained from a different CT scanner were used for the external validation cohort. The subjects were divided into three categories according to the quantitative CT (QCT) examination, namely, normal group, osteopenia group, and osteoporosis group. Firstly, a deep learning–based segmentation model was constructed. Then, classification models were established and selected, and then, an optimal model to build bone density value prediction regression model was chosen.ResultsThe DSC value was 0.951 ± 0.030 in the testing dataset and 0.947 ± 0.060 in the external validation cohort. The multi-feature fusion model based on the lumbar 1 vertebra had the best performance in the diagnosis. The area under the curve (AUC) of diagnosing normal, osteopenia, and osteoporosis was 0.992, 0.973, and 0.989. The mean absolute errors (MAEs) of the bone density prediction regression model in the test set and external testing dataset are 8.20 mg/cm3 and 9.23 mg/cm3, respectively, and the root mean square errors (RMSEs) are 10.25 mg/cm3 and 11.91 mg/cm3, respectively. The R-squared values are 0.942 and 0.923, respectively. The Pearson correlation coefficients are 0.972 and 0.965.ConclusionThe multi-feature fusion DCNN model based on only the lumbar 1 vertebrae and clinical variables can perform bone density three-classification diagnosis and estimate volumetric bone mineral density. If confirmed in independent populations, this automated opportunistic chest CT evaluation can help clinical screening of large-sample populations to identify subjects at high risk of osteoporotic fracture.

Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current < 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50–56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.

Evaluation of the 'A walk can work wonders' mass media campaign in South Australia.

Few population-wide efforts have targeted physical activity in Australia. Mass media campaigns are used to promote physical activity and walking, and World Health Organisation recommends their linkage to broader cross-government initiatives. This project evaluates a South Australian (SA) walking mass media campaign linked to the overall SA Walking Strategy. Between January and June 2022, Wellbeing SA (a SA government agency) conducted two waves of a mass media campaign, 'A walk can work wonders', to promote walking. The campaign was part of the SA Walking Strategy, targeting the goal to change the 'community culture towards recognising walking' as convenient, affordable and health-promoting. Evaluation was formative (pre-campaign), process (campaign implementation and delivery) and impact. Formative evaluation comprised concept and tagline testing with adult SAs to develop the campaign messages and theme. Process evaluation was comprised of media monitoring of social media, mainstream media and other media monitoring metrics. The process evaluation was dominated by social media marketing in Wave 1, with substantially more paid TV media and radio in Wave 2. The impact evaluation comprised two independent sample surveys of adult SAs (n = 800 each) following each wave of the campaign. Measures included generic and prompted campaign recall and attitudes to physical activity. An independent South Australian Population Health Survey (SAPHS) tracked walking and physical activity behaviours from 2021 to the end of 2022. Process evaluation showed intensive social media usage in Wave 1, and through increased paid television and radio in Wave 2. Generic recall of any walking message (23.9%) and prompted recall of the specific campaign message (27.8%) reached most socio-demographic groups, especially those with chronic health problems. Increases in intention to increase activity and increased self-reported activity were seen between the campaign Wave 1 and Wave 2 notable as the Wave 2 increase followed substantial television advertising. The SAPHS data showed increased population walking following the campaign waves, compared to the same period in 2021. Detailed and structured evaluation of a comprehensive mass media campaign showed good reach, and population changes in intentions and walking behaviour amongst SAs. SO WHAT?: It is likely that comprehensive approaches are needed to support mass media campaigns and amplify their effects. Serial, sustained campaigns are needed to monitor ongoing effects.

Cy-1, a major QTL for tomato leaf curl New Delhi virus resistance, harbors a gene encoding a DFDGD-Class RNA-dependent RNA polymerase in cucumber (Cucumis sativus)

BackgroundTomato leaf curl New Delhi virus (ToLCNDV) (family Geminiviridae, genus Begomovirus) is a significant threat to cucumber (Cucumis sativus) production in many regions. Previous studies have reported the genetic mapping of loci related to ToLCNDV resistance, but no resistance genes have been identified.ResultsWe conducted map-based cloning of the ToLCNDV resistance gene in cucumber accession No.44. Agroinfiltration and graft-inoculation analyses confirmed the resistance of No.44 to ToLCNDV isolates from the Mediterranean and Asian countries. Initial mapping involving two rounds of phenotyping with two independent F2 populations generated by crossing the begomovirus-susceptible cultivar SHF and No.44 consistently detected major quantitative trait loci (QTLs) on chromosomes 1 and 2 that confer resistance to ToLCNDV. Fine-mapping of Cy-1, the dominant QTL on chromosome 1, using F3 populations narrowed the candidate region to a 209-kb genomic segment harboring 24 predicted genes. Among these genes, DFDGD-class RNA-dependent RNA polymerase (CsRDR3), an ortholog of Ty-1/Ty-3 of tomato and Pepy-2 of capsicum, was found to be a strong candidate conferring ToLCNDV resistance. The CsRDR3 sequence of No.44 contained multiple amino acid substitutions; the promoter region of CsRDR3 in No.44 had a large deletion; and the CsRDR3 transcript levels were greater in No.44 than in SHF. Virus-induced gene silencing (VIGS) of CsRDR3 using two chromosome segment substitution lines harboring chromosome 1 segments derived from No.44 compromised resistance to ToLCNDV.ConclusionsForward and reverse genetic approaches identified CsRDR3, which encodes a DFDGD-class RNA-dependent RNA polymerase, as the gene responsible for ToLCNDV resistance at the major QTL Cy-1 on chromosome 1 in cucumber. Marker-assisted breeding of ToLCNDV resistance in cucumber will be expedited by using No.44 and the DNA markers developed in this study.

Kaposi's sarcoma-associated herpesvirus infection and its association with all-cause and cardiovascular mortality in the general adults: A prospective cohort study.

To investigate the association between Kaposi's sarcoma-associated herpesvirus (KSHV)/human herpesvirus 8 (HHV8) infection and both all-cause and cardiovascular mortality in a representative cohort of US adults, data from the National Health and Nutrition Examination Survey III (NHANES III; 1988‒1994) were analyzed, including 13,993 participants aged 18‒90 years who underwent KSHV serology evaluations. Mortality outcomes were ascertained through December 2019 using the National Death Index. Cox proportional hazards models were employed to examine the association between KSHV seropositivity and mortality, adjusting for potential confounders such as age, sex, ethnicity, body mass index, and serum TG. Over a median follow-up period of 26.5 years, 5503 deaths were recorded. KSHV seropositivity was associated with an increased hazard of all-cause mortality (Hazard Ratio [HR]: 1.32, 95% Confidence Interval [CI]: 1.03‒1.69) and cardiovascular mortality (HR: 1.58, 95% CI: 1.00‒2.50) after adjusting for age, sex, ethnicity, and body mass index. Notably, the association between KSHV infection and all-cause mortality persisted among women (HR: 1.32, 95% CI: 1.02‒1.72) after adjusting for all confounders, whereas the association with cardiovascular mortality was only statistically significant for men (HR: 1.90, 95% CI: 1.02, 3.53).KSHV infection may represent an independent risk factor for all-cause and cardiovascular mortality among US adults. These findings highlight the need for further research to validate these associations in independent populations and to elucidate the biological mechanisms underlying the observed increased mortality associated with KSHV infection.

Development and validation of a hierarchical approach for lymphoma classification using immunohistochemical markers.

Accurate lymphoma classification is critical for effective treatment and immunohistochemistry is a cost-effective and time-saving approach. Although several machine learning algorithms showed effective results, they focused on a specific task of classification but not the whole classification workflow, thus impractical to be applied in clinical settings. Thus, we aim to develop an effective and economic machine learning-assisted system that can streamline the lymphoma differential diagnostic workflow using EBER insitu hybridization and immunohistochemical markers. We included pathological reports diagnosed as lymphomas from two cancer centers (Sun Yat-sen University Cancer Center and Peking University Cancer Hospital & Institute). We proposed a hierarchical approach that mimicked the human diagnostic process and employed simplified panels of markers to perform a series of interpretable classification. The diagnostic accuracy for lymphoma pathological subtypes and the markers saving ratio were investigated in both temporal independent population and external medical center. A total of 14,927 patients and corresponding immunohistochemical results from two cancer centers were included. The proposed system had high discriminative ability for differentiating lymphoma pathological subtypes (measured by mean AUC in three validation cohorts, non-Hodgkin and Hodgkin lymphoma: 0.959; non-Hodgkin subtypes: 0.983; B-lymphoma subtypes: 0.868; T-lymphoma subtypes: 0.962; DLBCL subtypes: 0.957). In addition, the system's well selected characteristics can contribute to the development of agreement on panels of markers for differential diagnosis and help minimize cost of immunohistochemical marker techniques (measured by marker saving ratio compared to real clinical settings, internal primary-stage cohort: 16.45% saved, p < 0.001; internal later-stage cohort: 21.73% saved, p < 0.001; external cohort: 3.67% saved, p < 0.001). Machine learning-based hierarchical system using EBER insitu hybridization and IHC markers was developed, which could streamline the workflow by sequentially determining each lymphoma pathological subtype. The proposed system proved to be effective and cost-saving in independent and external validation, thus could be adopted affordably in future clinical practice.

Early detection of ovarian cancer using cell-free DNA fragmentomes and protein biomarkers.

Ovarian cancer is a leading cause of death for women worldwide in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker (CA-125 and HE4) analyses to evaluate 591 women with ovarian cancer, benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivity of 72%, 69%, 87%, and 100% for stages I-IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100% of ovarian cancers for stages I-IV. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC=0.88, 95% CI=0.83-0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation.

Demographic history of Platypus quercivorus (Coleoptera: Curculionidae), a beetle causing mass mortality of oak trees, in eastern Japan

Abstract In recent years, Japanese oak wilt causing mass mortality of oak trees has exhibited a significant geographical expansion. Understanding the formation history of current local populations of Platypus quercivorus Murayama, a vector of Japanese oak wilt, is important foundational knowledge for effectively preventing forest damage. In this study, we used genome-wide single nucleotide polymorphism data to infer the population genetic structure and historical demography of P. quercivorus populations in eastern Japan, where Japanese oak wilt is rapidly expanding. We found at least 3 distinct genetic groups, each of which caused Japanese oak wilt in different areas. The supported scenario suggests that 1 of 2 genetic groups in the northeast first diverged from the ancestral population, then the other group diverged from the southwestern population, followed by gene flow between the 2 groups. Recent admixtures of the 2 northeastern groups were identified at certain sites. The geographic genetic structure of the populations suggested that each genetic group experienced recent range expansion. Although genetically independent populations had caused oak tree mass mortality in their original distribution range, our results support the hypothesis that the recent geographic expansion of Japanese oak wilt outbreaks is attributed to the range expansion of these P. quercivorus groups.

Identification of Primary Organophosphate Esters Contributing to Enhanced Risk of Gestational Diabetes Mellitus Based on a Case-Control Study.

Epidemiological studies on associations of organophosphate ester (OPE) exposure and gestational diabetes mellitus (GDM) risk, which remain rare and inconclusive, were carried out with a case-control population comprising 287 GDM and 313 non-GDM pregnant women recruited from Tianjin. The GDM group suffered distinctly higher serum concentrations of tri-n-butyl phosphate (TNBP), tri(2-butoxyethyl) phosphate (TBOEP), triphenyl phosphate (TPHP), tri-iso-propyl phosphate (TIPP), and tri(1-chloro-2-propyl) phosphate (TCIPP) than the healthy control group (p < 0.001). Traditional analysis methods employed for either individual or mixture effects found positive correlations (p < 0.05) between the concentrations of five OPEs (i.e., TNBP, TBOEP, TPHP, TIPP, and TCIPP) and the incidence of GDM, while 2-ethylhexyl diphenyl phosphate, tri(1-chloro-2-propyl) phosphate, and bis(2-ethylhexyl) phosphate exhibited opposite effects. Three machine learning methods considering the concurrence of OPE mixture exposure and population characteristics were applied to clarify their relative importance to GDM risk, among which random forest performed the best. Several OPEs, particularly TNBP and TBOEP ranking at the top, made greater contributions than some demographical characteristics, such as prepregnancy body mass index and family history of diabetes, to the occurrence of GDM. This was further validated by another independent case-control population obtained from Hangzhou.