Tag: nevin manimala

Adv Ther. 2025 Dec 3. doi: 10.1007/s12325-025-03424-z. Online ahead of print.

ABSTRACT

INTRODUCTION: Cluster headache (CH) is a debilitating condition with health-related and economic burden to patients, including reduced quality of life (QoL) and suicidality. Brazilian data on comorbidities, symptoms, or impact on QoL and productivity remain limited. This study aims to assess CH’s impact on patients’ QoL, overall health, and employment in Brazil from physicians’ and patients’ perspectives.

METHODS: Data were extracted from the Adelphi Real World CH Disease Specific Programme™, a real-world, cross-sectional survey of physicians and their patients with CH in Brazil from November 2021-July 2022. Physicians reported on demographics, clinical characteristics, comorbidities, and suicidality. Patients reported CH’s impact on feelings/reactions, suicidality, employment using the Work Productivity and Activity Impairment questionnaire, and QoL using the EuroQol 5 Dimension 5 Level survey. Data analyses were conducted with R Statistical software (v4.1.2).

RESULTS: Patients completed 177 forms, while physicians completed 450 forms. Patients had episodic CH (ECH, n = 355) and chronic CH (CCH, n = 95). Common comorbidities were anxiety, insomnia, hypertension, depression, and dyslipidemia. Stress and lack of sleep were key physician-reported CH triggers, and 10.34% of patients had considered suicide. The presence of CH may have led to absenteeism (11.45%) and presenteeism (30.08% overall work impairment, 29.38% activity impairment, and 29.36% impairment while working).

CONCLUSIONS: CH carries a significant disease burden and negative socioeconomic impact, especially among patients with CCH. Stress was the most common trigger for both attacks and bouts with notable rates of suicidality. This indicates a need among physicians and patients for targeted and more efficacious interventions to reduce CH’s burden, improving patients’ QoL.

PMID:41335330 | DOI:10.1007/s12325-025-03424-z

Adv Ther. 2025 Dec 3. doi: 10.1007/s12325-025-03410-5. Online ahead of print.

ABSTRACT

INTRODUCTION: Esophageal squamous cell carcinoma (ESCC) accounts for approximately 90% of all esophageal cancer cases and is associated with poor prognosis. However, recent advancements have transformed the treatment landscape. Tislelizumab, a humanized immunoglobulin G4 (IgG4) anti-programmed cell death protein 1 (PD-1) monoclonal antibody, was developed to overcome resistance mechanisms by minimizing binding to FcγR on macrophages. The RATIONALE-302 clinical trial showed statistically significant survival benefits of tislelizumab over chemotherapy in second-line ESCC highlighting the necessity of evaluating comparative efficacy with existing treatments. This study aimed to identify trials evaluating anti-PD-1 therapies for second-line ESCC and indirectly estimate the relative efficacy of tislelizumab versus existing anti-PD-1 therapies.

METHODS: A systematic literature review (SLR) was originally conducted in 2021 then updated in 2022 and 2023. A feasibility assessment (FA) was undertaken to evaluate required assumptions for indirect treatment comparisons (ITCs) and determined that anchored simulated treatment comparisons (STCs) were the most appropriate methodology. Assessed outcomes included overall survival (OS), progression-free survival (PFS), and grade ≥ 3 treatment-related adverse events (TRAEs). Analyses were conducted in the hazard ratio scale for OS and PFS and in the odds ratio scale for TRAEs, whilst uncertainty was expressed in 95% confidence intervals.

RESULTS: The SLR identified 13 studies, six of which evaluated immunotherapies and were included in the FA. All studies were deemed similar and considered in the ITC, except for RAMONA, which differed substantially in study design, inclusion criteria, and patient characteristics. Indirect estimates obtained from the STCs were not statistically significant, except for the comparison of TRAEs with tislelizumab versus camrelizumab, where tislelizumab was more favorable.

CONCLUSIONS: Tislelizumab appears comparable to existing anti-PD-1 therapies (nivolumab, pembrolizumab, camrelizumab, and sintilimab) in OS, PFS, and TRAEs of grade ≥ 3 for patients receiving second-line treatment for ESCC with a potentially more favorable TRAE grade ≥ 3 profile than camrelizumab that requires confirmation.

TRIAL REGISTRATION: NCT03430843.

PMID:41335329 | DOI:10.1007/s12325-025-03410-5

Eur Arch Paediatr Dent. 2025 Dec 3. doi: 10.1007/s40368-025-01145-z. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the sequelae of traumatic dental injuries to the primary dentition in permanent successors and identify associated factors.

METHODS: A retrospective observational study was conducted based on the analysis of dental records of children who suffered traumatic dental injuries to primary teeth and received care and clinical follow-up from 2007 to 2023 at a specialised dental trauma clinic in Belo Horizonte, Brazil. Data were collected on aspects related to the children, their families, injuries to primary teeth, treatments performed, and consequences for the permanent dentition. Statistical analysis involved descriptive statistics, the chi-square test, and Poisson regression with robust variance (p < 0.05; 95% CI).

RESULTS: A total of 324 teeth from 154 children were assessed. A total of 25% of the permanent successors of injured primary teeth had sequelae, the most common being defects in enamel formation (n = 70). Children up to three years of age at the time of the trauma had a higher risk of sequelae in their permanent successors (PR = 1.64; 95% CI: 1.12-2.40) and those who received delayed treatment (RR = 1.66; 95% CI: 1.01-2.73). Injuries occurring on the street were also associated with a greater risk of sequelae (RR = 1.97; 95% CI: 1.20-3.23). No statistically significant differences were found between dental sequelae in permanent successors and the different types of luxation or fractures.

CONCLUSION: Defects in enamel formation were the most common sequelae after trauma in primary teeth. Sequelae in permanent successors were associated with the age of the child, the environment in which the injury occurred, and delayed post-injury care.

PMID:41335324 | DOI:10.1007/s40368-025-01145-z

J Math Biol. 2025 Dec 3;92(1):5. doi: 10.1007/s00285-025-02322-w.

ABSTRACT

Cholera is an acute diarrheal disease caused by the bacterium Vibrio cholerae. With the consideration of the transmission mechanism and heterogeneity of population, an age-structured cholera epidemic model is proposed, involving saturation incidence rates that describe direct and indirect transmission pathways and all class-ages with the susceptible age of susceptible individuals, infection age of infected individuals and biological age of Vibrio cholerae. The focus is to investigate the global dynamics of the model by using the basic reproduction number $R_0$ . After establishing the well-posedness of the initial-boundary value problem of the model, we study the existence of endemic steady state and local stability of the disease-free steady state in terms of $R_0$ . Next asymptotic smoothness of the semi-flow is discussed in order to obtain the existence of a global attractor. Finally, global stability of the disease-free and endemic steady states is obtained by combining Volterra-type Lyapunov functionals and existence of global attractors. Numerical simulations are given to demonstrate the effect of age structures and to illustrate the theoretical results.

PMID:41335303 | DOI:10.1007/s00285-025-02322-w

Brain Inform. 2025 Dec 3. doi: 10.1186/s40708-025-00281-y. Online ahead of print.

ABSTRACT

Brain-Computer Interfaces provide promising alternatives for detecting stress and enhancing emotional resilience. This study introduces a lightweight, subject-independent method for detecting stress during arithmetic tasks, designed for low computational cost and real-time use. Stress detection is performed through ElectroEncephaloGraphy (EEG) signal analysis using a simplified processing pipeline. The method begins with preprocessing the EEG recordings to eliminate artifacts and focus on relevant frequency bands (α, β, and γ). Features are extracted by calculating band power and its deviation from a baseline. A statistical thresholding mechanism classifies stress and no-stress epochs without the need for subject-specific calibration. The approach was validated on a publicly available dataset of 36 subjects and achieved an average accuracy of 88.89%. The method effectively identifies stress-related brainwave patterns while maintaining efficiency, making it suitable for embedded and wearable devices. Unlike many existing systems, it does not require subject-specific training, enhancing its applicability in real-world environments.

PMID:41335297 | DOI:10.1186/s40708-025-00281-y

Clin Rheumatol. 2025 Dec 3. doi: 10.1007/s10067-025-07799-w. Online ahead of print.

ABSTRACT

INTRODUCTION: METHODS: We used Mendelian randomization (MR) methods, including inverse-variance weighted (IVW), MR-Egger, and weighted median (WM) models, along with metabolic pathway analysis, meta-analysis, colocalization analysis, and genetic correlation studies to explore the relationship between plasma metabolites and SS.

RESULTS: Our analysis uncovered 53 metabolites with potential causal links to SS, among which nine demonstrated statistically significant associations. In the validation stage, two metabolites were found to play a role in SS pathogenesis: gluconate, which exhibited a protective effect, and 1,3,7-trimethylurate, which was associated with increased risk. The reliability of these results was further reinforced by sensitivity analyses and validation procedures. Additionally, metabolic pathway analysis identified four key pathways associated with SS risk: cysteine and methionine metabolism, glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, and oxaloacetate and dicarboxylate metabolism. Although no genetic correlations were identified, colocalization analysis suggested that the top single nucleotide polymorphism (SNP) in the LINC01572 gene may contribute to increased SS risk.

CONCLUSION: These findings provide novel insights into the metabolic etiology of SS, highlighting both protective and harmful metabolites.

KEY POINTS: • Identified 53 metabolites linked to Sjögren’s syndrome (SS), with 9 showing significant associations. • Validated gluconate (protective) and 1,3,7-trimethylurate (risk-increasing) in SS pathogenesis. • Found four key metabolic pathways associated with SS risk. • First two-sample MR study to assess plasma metabolites and SS risk using the largest GWAS dataset.

PMID:41335288 | DOI:10.1007/s10067-025-07799-w

J Turk Ger Gynecol Assoc. 2025 Dec 3;26(4):276-283. doi: 10.4274/jtgga.galenos.2025.2025-7-2.

ABSTRACT

OBJECTIVE: To assess the diagnostic performance of the ultrasound-based #Enzian classification in comparison with laparoscopic surgical findings in patients with endometriosis.

MATERIAL AND METHODS: This retrospective cohort study included patients who underwent laparoscopic excisional surgery for endometriosis between September 2023 and October 2024. Preoperative transvaginal ultrasound assessments were performed using the International Deep Endometriosis Analysis protocol, with findings recorded according to the updated #Enzian classification. Diagnostic performance was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy. Statistical analyses were conducted using SPSS version 26.0.0.0, with statistical significance set at p<0.05.

RESULTS: The study included 66 patients. The #Enzian classification demonstrated the highest diagnostic accuracy in compartments FA and FB (98.82% and 98.59%, respectively), both with perfect sensitivity and minimal false positives. The left ovary (O left) also showed strong performance (92.87% accuracy). In contrast, compartment A had low sensitivity (12.12%) despite a low false-positive rate. Compartments B left and C exhibited good accuracy (86.82% and 91.88%), with minimal false positives and moderate sensitivity. Variable results were observed in compartments O right and T. Although sensitivity was incomplete for FU, FI, and FO, specificity remained high across these subgroups.

CONCLUSION: The #Enzian ultrasound classification provides a reliable diagnostic framework, demonstrating high accuracy across multiple compartments. It is recommended that future studies include larger sample sizes and longitudinal design to further validate these findings.

PMID:41334622 | DOI:10.4274/jtgga.galenos.2025.2025-7-2

Int J Gynaecol Obstet. 2025 Dec 3. doi: 10.1002/ijgo.70696. Online ahead of print.

ABSTRACT

OBJECTIVE: This study identifies and describes global caesarean section (CS) training practices, comparing high-income countries (HIC) and low- and middle-income countries (LMIC).

METHODS: A convergent parallel mixed-methods study was conducted with a cross-sectional survey. The survey was distributed through professional networks and social media. Participation was voluntary and anonymous.

RESULTS: A total of 411 participants from 42 countries were included, with 42% (172) representing HIC and 58% (239) LMIC. Most participants were working in obstetrics and gynecology as specialists (52%, 214) or trainees (26%, 107). Participants from LMIC performed more CS annually, with a mean of 138 (±221) cases, compared to those from HIC with 44 (±64) cases (P < 0.001). Most were taught by an apprenticeship model (75%, 310). Feedback practices were predominantly informal, reported by 64% (263), while formal competence assessment was reported by 22% (38/172) of HIC participants and 9% (21/239) from LMIC (P < 0.001). Participants from LMIC completed fewer supervised cases compared to their HIC counterparts, with a median of 10 (interquartile range 5-20) compared to 50 (interquartile range 30-100) (P < 0.001). LMIC participants reported a higher incidence of major complications or mortality during training: 11% (24/202) versus 3% (3/120). Seventy percent (174/250) of the participants advocated for a formal training program for CS, suggesting that it could improve the quality and safety of CS.

CONCLUSION: The study highlights current practices and differences in CS training in LMIC and HIC. The outcomes associated with CS are influenced by multiple patient- and system-level factors, including access to care, patient risk profiles, and resources. However, training remains an essential and modifiable component, which, according to participants in this study, could be strengthened by incorporating evidence-based educational practices.

PMID:41334611 | DOI:10.1002/ijgo.70696

Int J Dent. 2025 Nov 23;2025:1485407. doi: 10.1155/ijod/1485407. eCollection 2025.

ABSTRACT

BACKGROUND/AIM: This study aimed to conduct a retrospective epidemiological investigation of patients treated in an extension project at a Brazilian dental school over the past 13 years.

MATERIAL AND METHODS: Clinical records of patients treated at a university hospital in Brazil as part of a specialized dental trauma care project were reviewed. The study included both primary and permanent teeth and covered the period from 2011 to 2024. Statistical analysis was conducted using the Pearson chi-square, with a significance level set at 5%.

RESULTS: Of the 460 records evaluated, 375 met the inclusion criteria, encompassing a total of 833 affected teeth (220 primary and 613 permanent teeth). Males (n = 248) represented the majority of individuals treated and exhibited a higher prevalence of hard tissue injuries (n = 208) compared with females (n = 93). The most common type of hard tissue injury was enamel and dentin fractures without pulp exposure (n = 139). Patients with hard tissue injuries generally sought care promptly after the traumatic event (p < 0.0001) and showed a significantly higher incidence of endodontic treatment needs (p < 0.0001) than those soft tissue fractures. Falls were identified as the leading cause of all types of hard tissue fractures (p < 0.0001).

CONCLUSIONS: The study identifies a high-risk profile for hard issue injuries, predominantly affecting children from infancy to early adolescence (ages 0-14 years), with falls being the most frequent cause. Additionally, hard tissue injuries were associated with faster care-seeking behavior and a higher likelihood of requiring endodontic treatment.

PMID:41334568 | PMC:PMC12665489 | DOI:10.1155/ijod/1485407

Front Physiol. 2025 Nov 17;16:1668129. doi: 10.3389/fphys.2025.1668129. eCollection 2025.

ABSTRACT

STUDY OBJECTIVE: Acute sleep deprivation significantly impacts cognitive function, contributes to accidents, and increases the risk of chronic illnesses, underscoring the need for reliable and objective diagnosis. Our work aims to develop a machine learning-based approach to discriminate between EEG recordings from acutely sleep-deprived individuals and those that are well-rested, facilitating the objective detection of acute sleep deprivation and enabling timely intervention to mitigate its adverse effects.

METHODS: Sixty-one-channel eyes-open resting-state electroencephalography (EEG) data from a publicly available dataset of 71 participants were analyzed. Following preprocessing, EEG recordings were segmented into contiguous, non-overlapping 20-second epochs. For each epoch, a comprehensive set of features was extracted, including statistical descriptors, spectral measures, functional connectivity indices, and graph-theoretic metrics. Four machine learning classifiers – Light Gradient-Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Classifier (SVC) – were trained on these features using nested stratified cross-validation to ensure unbiased performance evaluation. In parallel, three deep learning models-a Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and Transformer-were trained directly on the raw multi-channel EEG time-series data. All models were evaluated under two conditions: (i) without subject-level separation, allowing the same participant to contribute to both training and test sets, and (ii) with subject-level separation, where models were tested exclusively on unseen participants. Model performance was assessed using accuracy, F1-score, and area under the receiver operating characteristic curve (AUC).

RESULTS: Without subject-level separation, CNN achieved the highest accuracy (95.72%), followed by XGBoost (95.42%), LightGBM (94.83%), RF (94.53%), and SVC (85.25%), with the Transformer (77.39%) and LSTM (66.75%) models achieving lower accuracies. Under subject-level separation, RF achieved the highest accuracy (68.23%), followed by XGBoost (66.36%), LightGBM (66.21%), CNN (65.35%), and SVC (65.08%), while the Transformer (63.35%) and LSTM (61.70%) models achieved the lowest accuracies.

CONCLUSION: This study demonstrates the potential of EEG-based machine learning for detecting acute sleep deprivation, while underscoring the challenges of achieving robust subject-level generalization. Despite reduced accuracy under cross-subject evaluation, these findings support the feasibility of developing scalable, non-invasive tools for sleep deprivation detection using EEG and advanced ML techniques.

PMID:41334558 | PMC:PMC12665582 | DOI:10.3389/fphys.2025.1668129