Category: Statistics

CNS Drugs. 2026 Jun 15. doi: 10.1007/s40263-026-01302-y. Online ahead of print.

ABSTRACT

BACKGROUND: TV-46000 (Uzedy^®) is a long-acting injectable antipsychotic for subcutaneous use that combines risperidone and an innovative, copolymer-based drug delivery technology in a suspension that was approved as a treatment for schizophrenia and bipolar I disorder by the US Food and Drug Administration (FDA) in April 2023 and October 2025, respectively. It is a risperidone injectable that can be administered once monthly or once every 2 months. As part of the clinical development of TV-46000, a Phase 1 open-label dose escalation study (TV46000-SAD-10055) was conducted to evaluate the safety and pharmacokinetics (PK) of TV-46000 in participants with schizophrenia or schizoaffective disorder.

METHODS: Individuals diagnosed with schizophrenia or schizoaffective disorder were assigned to 1 of 8 cohorts, which consisted of single escalating doses (50 to 225 mg) or 3 multiple doses (75 mg or 150 mg) administered every month for 3 months. Every cohort was preceded by a run-in period of 7 days with daily oral risperidone (2-6 mg) followed by a 7-day washout period before the TV-46000 injection. TV-46000 was administered in the abdomen in all but one cohort, where 225 mg was administered in the upper arm, to assess interchangeability between injection sites. Outcome measures reported were adverse events (AEs), local tolerability, changes in neurological and clinical assessments, and PK measures (determined by risperidone and 9-OH risperidone total active moiety [TAM] concentrations).

RESULTS: Of the 194 participants screened, 99 were included in the study. Of the 99 participants, 13 discontinued the study, but none of the discontinuations were related to the treatment. Most participants were Black or African American men, with a mean age of 44.2 years and a mean body mass index of 28.53 kg/m². Treatment-emergent AEs after TV-46000 administration included injection-site reactions, increased blood creatine phosphokinase, and weight gain. There were two serious AEs; one death due to suicide and one exacerbation of schizophrenia requiring hospitalization that were both unrelated to TV-46000. After a single dose injection of TV-46000 50 to 225 mg, TAM concentrations reached clinically relevant plasma concentrations of 10 ng/mL within 24 hours for all doses and remained within therapeutic range for 28-56 days, depending on dose. The systemic exposure parameters (maximum, minimum, and average plasma concentration) after the third monthly TV-46000 injections of 75 and 150 mg were comparable with the corresponding steady-state exposure of 3-5-mg/day oral risperidone.

CONCLUSIONS: Overall, TV-46000 was well tolerated with no new safety signals detected compared with other formulations of risperidone. The PK profile of TV-46000 exhibited favorable characteristics of an extended-release formulation, including rapid initial release for fast onset of action and prolonged systemic exposure adequate for monthly administration.

PMID:42295697 | DOI:10.1007/s40263-026-01302-y

Vet Med Sci. 2026 Jul;12(4):e70922. doi: 10.1002/vms3.70922.

ABSTRACT

BACKGROUND: The main goal of this study was to identify various brightness, motion and Doppler echocardiographic variables in small-breed dogs with myxomatous mitral valve disease.

ANIMALS: Sixty client-owned small-breed dogs with mitral regurgitation murmurs.

METHODS: Echocardiography was performed in brightness, motion and Doppler modality from the right parasternal view. On the basis of thoracic radiographs, the dogs were categorized into two groups: those with cardiomegaly and those without cardiomegaly. Additionally, on the basis of the jet area signal to left atrium ratio in colour Doppler echocardiography, the canines were divided into three groups: mild, moderate and severe mitral regurgitation. Furthermore, the dogs were classified into preclinical and clinical groups on the basis of the presence or absence of clinical signs, and various variables were compared across these groups.

RESULTS: This difference was statistically significant and more prevalent in intact dogs than in spayed/neutered dogs. Significant differences were observed in variables including LAmax, LA:Ao, LVIDd, LVIDs, VMA, VMG, VMV, GMV, VMA-GMVT, LVOT and GLVOT in dogs with myxomatous mitral valve disease of varying mitral regurgitation severity and between normal and enlarged hearts. Significant differences were also found in the FS, GMV, VLVOT and GLVOT variables between the clinical and preclinical groups. A moderate and statistically significant correlation was observed between LA:Ao and VMA, VMV and LVOT. Weak and mostly insignificant correlations were found between the Doppler and motion-mode variables.

CONCLUSIONS: Varying mitral regurgitation severity, heart size and the presence of clinical signs can significantly affect certain brightness, motion and Doppler echocardiographic variables in dogs with myxomatous mitral valve disease (valvular heart disease).

PMID:42295688 | DOI:10.1002/vms3.70922

Ther Innov Regul Sci. 2026 Jun 15. doi: 10.1007/s43441-026-00999-9. Online ahead of print.

ABSTRACT

OBJECTIVE: Medical Information plays a critical role in delivering accurate, evidence-based responses to unsolicited requests. At the heart of the response is the content developed by Medical Information. Additionally, the metrics and key performance indicators (KPIs) surrounding content development and delivery are necessary to gauge the utilization and impact of the content. Because the landscape has evolved significantly since the last survey in 2018, the need to re-examine content development approaches and KPIs is warranted. The objective of this benchmarking survey was to assess the evolving approach and tactics of content development and utilization of KPIs of phactMI member companies with a comparison to 2018 where applicable.

METHODS: In May 2024, an electronic survey containing 71 closed and open-ended questions was distributed to 36 phactMI member companies. The survey included demographics, development of specific content, scientific response documents (SRDs), custom response documents, and metrics and KPI. Results were analyzed with descriptive statistics.

RESULTS: Medical Information teams primarily develop content for HCPs (90%) versus patients (10%). Disease state content creation was more common among small/midsize companies (36%) than large companies (18%), though requests for such content were infrequent. Similar to 2018, SRDs remain predominantly traditional written documents (66%), with emerging use of frequently asked questions, infographics, HTML, and slide decks. Most companies (85%) maintain centralized SRD development, but the proportion without target timelines for SRD development increased from 37% in 2018 to 72% in 2024. AI adoption is in early stages, with 3 companies actively using it and 18 exploring applications, primarily for writing and paraphrasing. Omnichannel integration is reported by 59% of companies, and custom response documents account for 25% of inquiries. Similar to 2018, the most common KPIs in medical information departments include inquiry volume, turnaround time, and document updates. There is a growing emphasis on customer satisfaction and digital engagement. HCPs most frequently use email (52%) and phone (29%) for information, while digital channels remain underutilized.

CONCLUSION: This benchmarking survey highlights the need for ongoing innovation, strategic alignment, and robust quality assurance in Medical Information content development and KPI reporting. While many things are similar to 2018, customer expectations continue to evolve and technology has advanced. Organizations must balance operational efficiency with personalized engagement, while ensuring compliance and ethical standards. For the future, the three areas to prioritize are establishing non-traditional content development formats, processes, and workflows; expanding digital and mobile optimization; and strengthening governance around AI and content reuse.

PMID:42295686 | DOI:10.1007/s43441-026-00999-9

Updates Surg. 2026 Jun 15. doi: 10.1007/s13304-026-02715-6. Online ahead of print.

ABSTRACT

This study aimed to evaluate the need for intensive care unit (ICU) admission and identify factors associated with in-hospital mortality (IHM) in patients with Fournier’s gangrene (FG) using traditional statistical methods complemented by machine learning-based models. This retrospective cohort study included surgically treated FG patients at a tertiary referral center. Demographic, clinical, laboratory, and perioperative variables were analyzed. Established prognostic indices, including the Fournier’s Gangrene Severity Index, Uludağ Fournier’s Gangrene Severity Index, Laboratory Risk Indicator for Necrotizing Fasciitis, Systemic Immune-Inflammation Index, platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio, were evaluated. Patients were stratified according to ICU requirement and survival status. Multivariable logistic regression was performed to identify independent predictors. In addition, exploratory machine learning models, including k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine, and Decision Tree algorithms, were applied to assess predictive performance. Multivariable logistic regression analysis revealed that PLR and heart failure (HF) were independent predictors of ICU requirement. Regarding IHM, PLR remained the only independent predictor. In the exploratory ML analysis, KNN and RF showed AUC values of 0.886 and 0.873 for ICU prediction, and 0.787 and 0.765 for IHM prediction, respectively. However, given the limited sample size and low number of outcome events, these performance estimates should be interpreted cautiously. This study highlights the prognostic relevance of inflammatory markers, particularly PLR, and comorbid conditions including HF, chronic kidney disease, cerebrovascular disease and concurrent malignancy, in disease severity and IHM in FG. Machine learning-based models showed promising performance, although these findings should be considered preliminary and require validation in larger, multicenter cohorts.

PMID:42295668 | DOI:10.1007/s13304-026-02715-6

Bull Math Biol. 2026 Jun 15;88(7):118. doi: 10.1007/s11538-026-01682-8.

ABSTRACT

Across ecosystems, autotroph growth and susceptibility to disease are strongly constrained by the availability of essential nutrients such as nitrogen and phosphorus. Understanding how nutrient availability influences disease transmission is important for predicting disease persistence, outbreak risk, and long-term ecosystem dynamics under changing environmental conditions. At the same time, infectious diseases in autotrophs can reshape ecosystem processes by altering elemental recycling and the nutrient supply available to hosts. Here, we formulate a five-dimensional deterministic system of nonlinear ordinary differential equations within a disease-mediated nutrient dynamic framework. We incorporate novel nutrient-driven transmission and nonlinear resource uptake kinetics to capture the bidirectional relationships linking elemental cycles with infectious disease in a natural forest ecosystem. Using a combination of qualitative mathematical analysis, including proofs of solutions boundedness and derivations of basic reproductive number, and numerical bifurcation analyses, we evaluate the system’s long-term behavior. Our results show that nutrient-disease feedbacks strongly regulate the distribution of host densities and nutrients between autotrophs and the abiotic environment. Incorporating nutrient-driven transmission reveals bifurcation structures distinct from frameworks with constant transmission, highlighting high sensitivity to nutrient availability and stronger nonlinear feedbacks. Bifurcation analyses indicate that nutrient enrichment lowers the transmission threshold for disease persistence and accelerates the onset of oscillatory dynamics with greater amplitude under high nutrient levels. Similarly, higher transmission rates reduce the nutrient threshold for disease persistence and shift oscillatory dynamics to emerge at lower nutrient levels. We further show that even small differences in infected host uptake rates strongly influence dynamics: lower uptake dampens oscillations and weakens feedbacks, whereas higher uptake amplifies bottom-up nutrient effects on disease and reinforces top-down effects on nutrient cycling, producing pronounced limit cycles in hosts, nutrients, and prevalence. Overall, nutrient-driven transmission alters thresholds and oscillatory regimes in ecosystem disease models, leading to dynamics that are not captured under constant transmission assumptions. This work advances applied ecosystem and ecological disease sciences by improving our understanding of disease transmission processes in plant communities.

PMID:42295654 | DOI:10.1007/s11538-026-01682-8

Clin Transl Oncol. 2026 Jun 15. doi: 10.1007/s12094-026-04455-w. Online ahead of print.

ABSTRACT

OBJECTIVES: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer (BC) characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression, and is one of the most challenging cancers to treat. Non-coding RNAs have been identified as potential biomarkers for various diseases, including cancer. Identifying these biomarkers may facilitate early diagnosis and improve treatment strategies. Therefore, this study aimed to evaluate the non-coding RNAs NEAT1 and miR-506-3p expression levels in TNBC patients.

METHODS: Formalin-fixed, paraffin-embedded (FFPE) tumor tissues and paired adjacent non-tumor tissues were obtained from 35 TNBC patients. Total RNA was extracted, and the expression levels of NEAT1 and miR-506-3p were quantified by qPCR using GAPDH and U6 as internal controls, respectively. Relative expression was analyzed by the 2^-ΔΔCt method. Statistical analyses included comparison of gene expression fold changes, assessment of associations with clinicopathological features, Spearman correlation analysis, and receiver-operating characteristic (ROC) curve analysis.

RESULTS: NEAT1 was significantly upregulated (≈eightfold, P < 0.0001) and miR-506-3p was markedly downregulated (≈fivefold, P < 0.0001) in tumor tissues compared to adjacent non-tumor samples. Elevated NEAT1 expression was significantly associated with the younger age group, necrosis, calcification status, larger tumor size, and vascular invasion, whereas no significant associations were observed with BMI, tumor grade, or lymph-node involvement (P > 0.05). MiR-506-3p expression showed no significant clinicopathological differences in subgroup analysis. A significant inverse correlation was observed between NEAT1 and miR-506-3p levels (rs = -0.33, P < 0.05). ROC curve analysis showed promising discriminatory performance for NEAT1 in the present cohort (AUC = 0.9563, 95% CI 0.9165-0.9961; estimated sensitivity = 1.00, specificity = 0.77) and moderate discriminatory performance for miR-506-3p (AUC = 0.7971, 95% CI 0.6946-0.8997). A logistic regression model combining both markers showed improved discrimination in this dataset (AUC = 0.9739).

CONCLUSION: Our findings suggest that NEAT1 and miR-506-3p exhibit opposite expression patterns in TNBC tissues and may serve as complementary tissue-based candidate diagnostic biomarkers. Their combined use showed improved discriminatory performance in this cohort; however, larger independent validation studies are required before a clinical diagnostic application can be proposed.

PMID:42295648 | DOI:10.1007/s12094-026-04455-w

Interdiscip Sci. 2026 Jun 15. doi: 10.1007/s12539-026-00857-0. Online ahead of print.

ABSTRACT

With the rapid development of artificial intelligence and medical image analysis, MRI-based automated diagnosis has provided an effective approach for Alzheimer’s disease (AD) assessment. To improve the performance of MRI-based AD classification, this study proposes an AD diagnosis model termed High-level CBAM-ResNet34. First, T1-weighted structural MRI data are preprocessed using a unified pipeline and further converted into two-dimensional slices for model training. Then, a ResNet34-based classification framework is constructed, in which the convolutional block attention module (CBAM) is introduced into the high-level feature stage to enhance discriminative feature representation. In addition, to better adapt to inter-dataset differences, the negative-class weight parameter is selected according to the empirical performance on each dataset during training. Experimental results on the ADNI dataset show that the proposed model achieved an AUC of 0.8757 and an accuracy of 0.8160, outperforming several representative comparison models in overall performance. Ablation and comparison experiments further verified the effectiveness of the proposed design, and external validation on the open access series of imaging studies 1 (OASIS 1) dataset demonstrated its generalization ability. These results indicate that the proposed model is effective for MRI-based AD diagnosis and provides a useful reference for computer-aided neuroimaging analysis.

PMID:42295635 | DOI:10.1007/s12539-026-00857-0

Pharmacoeconomics. 2026 Jun 15. doi: 10.1007/s40273-026-01631-2. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Health economic modelling integrates evidence from multiple sources and relies on transparency to support reimbursement decisions. Hyperlipidaemia is a major contributor to cardiovascular disease and is routinely evaluated within health technology assessment frameworks. This systematic review examines health economic models of hyperlipidaemia, evaluates the methodological approaches used in the model development and identifies opportunities to improve model quality and transparency.

METHODS: A systematic literature search was conducted in MEDLINE and Embase between 1987 and 2025 to identify hyperlipidaemia health economic models. Screening, data extraction and quality assessment were performed manually, and artificial intelligence (AI) software was used for additional checking, this was followed by conflict resolution. Findings are presented through narrative synthesis. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251043922). The review assessed key aspects of model structure including model type, type of hyperlipidaemia, population, hyperlipidaemia-related events, model outcomes, time horizon, software used and discounting approach. We assessed methodological quality through the Philips checklist, with a focus on model structure, data usage, the way in which studies addressed uncertainty through sensitivity analysis and model validation.

RESULTS: A total of 154 unique model-based economic evaluations were identified, comprising 132 Markov models, 9 microsimulation models and 1 discrete event simulation. Most economic evaluations explored general hypercholesterolaemia in 138 studies, followed by familial hypercholesterolaemia in 23 studies, while lipoprotein(a) was investigated in two studies. Primary prevention was examined in 89 models, secondary prevention in 50 models and a combination of both in 15 evaluations. Overall methodological quality was assessed as high for models’ structure and data usage; however, it was moderate for model consistency.

CONCLUSIONS: Hyperlipidaemia models generally had transparent assumptions and a justified structure. However, current models often lack systematic data-selection practices to identify the most appropriate evidence for evaluation. Extensive uncertainty analysis and model validation were frequently absent in the assessed models. To support decision-making, model results should be displayed in an open-source format with publicly available code. Furthermore, patient values are rarely incorporated in current modelling practices, representing missed opportunities for patient-centred care.

PMID:42295615 | DOI:10.1007/s40273-026-01631-2

Environ Monit Assess. 2026 Jun 15;198(7):723. doi: 10.1007/s10661-026-15369-z.

ABSTRACT

Urban food systems are at risk from microplastic contamination due to inadequate awareness among vendors and direct food contamination. In this integrated study, microplastics were detected in cauliflower from three high-traffic markets in Madurai, Tamil Nadu, India, along with a comprehensive survey of 111 food vendors to assess knowledge, risk perception, and behavioural intentions. In all market samples, microplastics were detected (9-16 particles per cauliflower), while they were absent from supermarket comparisons. Fourier Transform Infrared Spectroscopy (FTIR) analysis identified multiple polymer types including polystyrene, polypropylene, polytetrafluoroethylene, and polyethylene terephthalate, with additional polymer signatures detected requiring further confirmation. The survey among vendors revealed alarmingly low awareness (6.3%), concentrated exclusively in Villapuram market (χ² = 11.81, p = 0.003), with limited health risk perception (30.6%) and low willingness to adopt alternative packaging (18.0%). Factor analysis identified three behavioural dimensions: Environmental Consciousness, Health Concern, and Business Adaptability. It is alarming to note that vendors are not aware of contamination despite measurable contamination, which highlights the need for targeted interventions.

PMID:42295589 | DOI:10.1007/s10661-026-15369-z

Trop Anim Health Prod. 2026 Jun 15;58(5):339. doi: 10.1007/s11250-026-05092-9.

ABSTRACT

Livestock production in South Africa remains an important contributor to ensuring food security and provides many social and economic attributes to the country. Locally adapted and indigenous breeds are able to survive in extreme weather conditions and produce and reproduce for long periods of time. Cattle (non-descript) in these areas constitute a valuable genetic resource. The study was conducted to assess the genetic diversity and population structure of cattle populations in three regions of Gauteng. Hair samples were collected from Western Gauteng (n = 266), Lesedi (n = 132) and Tshwane (n = 69) regions. Direct polymerase chain reaction (PCR) was conducted using eleven International Society for Animal Genetics (ISAG) recommended bovine microsatellite markers. Fragment analysis was performed. Five local cattle breeds (n = 473) were included as reference populations (Afrikaner, Nguni, Brahman, Bonsmara and Drakensberger breeds). The Angus breed was included as an outgroup. Statistical analysis was performed using MS Toolkit, STRUCTURE and GenAlEx programs. High level of genetic diversity was found across the populations, with an average expected heterozygosity of 75%. Levels of inbreeding varied from – 0.031 to 0.042 with a mean of 0.015, illustrating low levels of inbreeding within the populations. The results from this study indicated that more genetic differentiation found within populations rather than between populations. These findings may serve as a baseline for the management of genetic resources and contribute in developing future breeding programmes in these municipalities.

PMID:42295579 | DOI:10.1007/s11250-026-05092-9