Category: Nevin Manimala

J Neurosurg. 2025 Feb 21:1-10. doi: 10.3171/2024.10.JNS241527. Online ahead of print.

ABSTRACT

OBJECTIVE: Giant pituitary adenomas (GPAs) are challenging skull base tumors due to their size and proximity to critical neurovascular structures. Achieving gross-total resection (GTR) can be difficult, and residual tumor burden is commonly reported. This study evaluated the ability of convolutional neural networks (CNNs) to predict the extent of resection (EOR) from preoperative MRI with the goals of enhancing surgical planning, improving preoperative patient counseling, and enhancing multidisciplinary postoperative coordination of care.

METHODS: A retrospective study of 100 consecutive patients with GPAs was conducted. Patients underwent surgery via the endoscopic endonasal transsphenoidal approach. CNN models were trained on DICOM images from preoperative MR images to predict EOR, using a split of 80 patients for training and 20 for validation. The models included different architectural modules to refine image selection and predict EOR based on tumor-contained images in various anatomical planes. The model design, training, and validation were conducted in a local environment in Python using the TensorFlow machine learning system.

RESULTS: The median preoperative tumor volume was 19.4 cm3. The median EOR was 94.5%, with GTR achieved in 49% of cases. The CNN model showed high predictive accuracy, especially when analyzing images from the coronal plane, with a root mean square error of 2.9916 and a mean absolute error of 2.6225. The coefficient of determination (R2) was 0.9823, indicating excellent model performance.

CONCLUSIONS: CNN-based models may effectively predict the EOR for GPAs from preoperative MRI scans, offering a promising tool for presurgical assessment and patient counseling. Confirmatory studies with large patient samples are needed to definitively validate these findings.

PMID:39983104 | DOI:10.3171/2024.10.JNS241527

JCO Precis Oncol. 2025 Jan;9:e2400491. doi: 10.1200/PO-24-00491. Epub 2025 Feb 21.

ABSTRACT

PURPOSE: Urinary catecholamine metabolites are well-known biomarkers for the diagnosis (Dx) of neuroblastoma, but their clinical significance in determining therapy response during treatment is not well established. Therefore, catecholamines are not included in criteria for assessing response and complete remission (CR). This study investigated the use of urinary catecholamines in response monitoring and predicting survival outcomes.

METHODS: From 2005 to 2021, a panel of eight urinary catecholamines were measured in patients with high-risk neuroblastoma at Dx and at standard evaluation moments during treatment. At the same time points, response and CR were assessed according to the revised International Neuroblastoma Response Criteria.

RESULTS: The total cohort consists of 153 high-risk patients, and at least one of the eight metabolites was elevated (ie, catecholamine status positive) in 141 of 146 (97%), 104 of 128 (81%), and 39 of 69 (57%) patients at Dx, postinduction, and at CR, respectively. Primary tumor resection significantly reduced catecholamine levels (P < .01). A positive catecholamine status at Dx, during treatment, and at the end of treatment was not significantly associated with event-free survival (EFS) or overall survival (OS). However, in patients who achieved CR, those with a positive catecholamine status had poor EFS (38% v 80%, respectively; P < .01) and OS (52% v 86%, respectively; P = .01) compared with those with a negative catecholamine status. Notably, 3-methoxytyramine levels at CR seem to be a prognostic marker for poor OS (hazard ratio, 7.5 [95% CI, 2.0 to 28.6]).

CONCLUSION: Catecholamine measurements contribute to the assessment of CR and identifies patients with high-risk neuroblastoma with an increased risk of relapse and death.

PMID:39983076 | DOI:10.1200/PO-24-00491

Neurology. 2025 Mar 25;104(6):e213414. doi: 10.1212/WNL.0000000000213414. Epub 2025 Feb 21.

ABSTRACT

OBJECTIVES: Despite efforts to reduce the gender wage gap, a 2019 survey of American Academy of Neurology members showed that a significant gender wage gap was still evident. This study aims to assess the gender wage gap from 2019 to 2023.

METHODS: Data were obtained from the American Association of Medical Colleges (AAMC) Faculty Salary Report from 2019 to 2023. Compensations of full-time neurology faculty at medical schools were recorded by gender and stratified by academic rank. Mean salaries and gender wage gaps were trended over time, and statistical significance was tested using linear regression models.

RESULTS: From 2019 to 2023, the mean salary increased from $278 ,475 to $313 ,627 for men (p < 0.001) and $231, 863 to $269 ,870 for women (p < 0.001). The average gender wage gap was $46, 612 in 2019 and $43, 757 in 2023. Women made 90 cents-on-the-dollar compared with men at the same academic rank in 2019, which remained unchanged at 91 cents-on-the-dollar in 2023 (p = 0.48).

DISCUSSION: Despite increases in academic neurology salaries for both men and women from 2019 to 2023, the gender wage gap remained unchanged.

PMID:39983058 | DOI:10.1212/WNL.0000000000213414

JMIR Public Health Surveill. 2025 Feb 21;11:e52928. doi: 10.2196/52928.

ABSTRACT

BACKGROUND: Nonsuicidal self-injury (NSSI) is a major public health problem leading to psychological problems in adolescents and young adults, similar to disorders such as depression and anxiety.

OBJECTIVE: The aims of this study were to investigate (1) the interaction between BMI and socioenvironmental factors (including chronotype and mental health) that contribute to NSSI, and (2) whether self-esteem plays a mediating role in this association.

METHODS: From May to June 2022, the multistage cluster sampling method was used to sample college students in four grades, including freshmen and seniors. The baseline participants were followed up 6 months later, excluding those who did not qualify, and the participants included 1772 college students. Socioenvironmental factors (chronotype/mental health), self-esteem, and NSSI were measured using a questionnaire. Multivariate linear regression models and chi-square analysis were used to evaluate the linear relationship between BMI, socioenvironmental factors, and self-esteem and the NSSI status. We use a process approach (mediation-moderation analysis) to explore the complex relationships between these variables.

RESULTS: The mean age of the participants was 20.53 (SD 1.65) years at baseline. A significant association was revealed, suggesting that a high BMI (β=.056, 95% CI 0.008-0.086, P=.018) was associated with a higher NSSI. There was also an interaction among BMI, socioenvironmental factors, and NSSI. Socioenvironmental factors played both moderating and mediating roles in the relationship between BMI and NSSI, whereas self-esteem only played a mediating role.

CONCLUSIONS: Paying attention to factors such as overweight and obesity is important for early BMI control to identify other potential risk factors for NSSI and to evaluate how self-esteem can be improved considering multiple perspectives to improve the effect of BMI on NSSI in adolescents.

PMID:39983049 | DOI:10.2196/52928

Ocul Immunol Inflamm. 2025 Feb 21:1-6. doi: 10.1080/09273948.2025.2469605. Online ahead of print.

ABSTRACT

PURPOSE: Depression is a significant burden for uveitis patients due to the chronic nature of their condition and associated vision impairment. Non-infectious uveitis (NIU), often linked to autoimmune diseases, frequently involves psychological distress. This study examines depression prevalence among NIU patients in Lebanon to address gaps in regional data.

METHODS: This cross-sectional study recruited 100 NIU patients and 100 controls from a tertiary care center in Lebanon. Depression was assessed using the Patient Health Questionnaire-2 (PHQ-2) and confirmed with the Patient Health Questionnaire-9 (PHQ-9) for positive screens. Demographics, disease characteristics, and treatment data were extracted from medical records. Statistical analyses included independent t-tests and multivariate analysis (p < 0.05).

RESULTS: Depression prevalence was 35% in NIU patients versus 26% in controls, though not statistically significant (p = 0.167). Corticosteroid usage correlated with higher depression risk (OR = 3.85 [95% CI: 1.33,11.2], p = 0.013), while longer uveitis duration correlated with lower depression risk (OR = 0.93 [95% CI: 0.89, 0.97], p < 0.001).

CONCLUSION: Depression is prevalent among NIU patients in Lebanon, particularly females and vulnerable demographics. Integrated care strategies addressing mental health within ophthalmology practices are essential to improve quality of life. Future research should explore additional psychological conditions and contributing factors.

PMID:39983038 | DOI:10.1080/09273948.2025.2469605

JBRA Assist Reprod. 2025 Feb 21. doi: 10.5935/1518-0557.20240098. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the effects of COVID-19 vaccination (AstraZeneca® and CoronaVac®) on anti-Müllerian hormone (AMH) levels in threatened women.

METHODS: Retrospective cohort study evaluating serum AMH before and up to three vaccination doses against COVID-19 between 2021 and 2022 at FMABC. Statistical analysis presented in Stata 14. Clinical variables were described by absolute and relative frequency, in addition to measures of central tendency and dispersion. Shapiro-Wilk test for normality. Continuous variables compared within the group using the Friedman test and, between groups, Mann-Whitney U tests (non-parametric); Chi-square and Fisher’s exact tests, for categorical variables, with p<0.05.

RESULTS: Median age of the 38 volunteers was 24 years (p25-75: 22-30) and AMH levels (ng/dl) at times 0, 1, 2 and 3 median (95% CI) were, respectively, 4.6(3.5-6); 4(2.3-5); 4.3(3-5); 4.9(2.6-6.3), p=0.726. Likewise, there was no statistically significant difference in the assessments between subgroups aged <35 and ≥35 years old and with and without exposure to COVID-19 in relation to AMH values.

CONCLUSIONS: The vaccination against COVID-19 with the AstraZeneca® and CoronaVac® vaccines did not indicate any damage to anti-Müllerian hormone values in women of reproductive age.

PMID:39983031 | DOI:10.5935/1518-0557.20240098

JBRA Assist Reprod. 2025 Feb 21. doi: 10.5935/1518-0557.20240095. Online ahead of print.

ABSTRACT

OBJECTIVE: Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in women of reproductive age, being one of the main causes of infertility. Anti-Müllerian hormone (AMH) is an important marker of ovarian reserve and has been proposed as an alternative criterion for the diagnosis of PCOS. This study verifies whether AMH and body mass index (BMI) values are predictors of pregnancy in infertile women with PCOS undergoing artificial insemination (AI), a less invasive and painless technique of assisted reproductive technologies (ART).

METHODS: This retrospective observational study involved 220 women with PCOS who underwent AI between 2010 and 2022. Participants were categorized into three groups based on BMI and serum AMH levels. To categorize the three AMH classes, the 25th (4.08ng/mL) and 75th (8.99ng/mL) AMH percentiles were defined as cut-offs, and the words ‘low’, ‘middle’, and ‘high’ were utilized to define the groups.

RESULTS: There was a tendency towards a decrease in reproductive outcomes (number of inseminations with positive human-chorionic gonadotropin, number of live births, and number of term births) with an increase in the BMI value. All of these outcomes were also slightly higher in women with ‘middle’ AMH levels compared to women with ‘low’ and ‘high’ AMH. However, none of these results were statistically significant.

CONCLUSIONS: This study suggests BMI may be an important predictive factor for pregnancy and there appears to be a range of biological normality for AMH values, where ‘low’ and ‘high’ levels of this hormone could constitute a marker of poor reproductive prognosis, in women with PCOS undergoing AI.

PMID:39983029 | DOI:10.5935/1518-0557.20240095

Traffic Inj Prev. 2025 Feb 21:1-5. doi: 10.1080/15389588.2025.2456942. Online ahead of print.

ABSTRACT

OBJECTIVES: Alcohol and excessive speeding are both linked to elevated crash risk. Alcohol-related and speeding-related crashes are recorded and treated as distinct, with separate etiologies. Yet, speeding and alcohol use are interrelated. We speculate that speeding might account for some of the crash risk associated with drunk driving.

METHODS: Data from the Crash Investigation Sampling System were analyzed. Vehicle speeds, measured moments before crashes, were estimated from driver blood alcohol concentrations (BACs) for different levels of injury severity. We first applied a previously published formula to estimate the relative crash risk associated with speeds that occur at different BACs. Then, from the literature we obtained relative crash risk odds ratios associated with different BACs. Finally, for BACs of 0.08 g/dl and 0.16 g/dl, separately for serious injury and fatality crashes, we created ratios to estimate what portion of the alcohol-crash risk might be attributed to higher travel speeds.

RESULTS: A statistically significant BAC × Injury Severity interaction indicated that crash drivers with higher BACs drove faster than their sober counterparts, and that this was exacerbated for more serious injuries. Among drivers with fatal injuries, those with BACs of 0.16 g/dl were traveling over 10 mph faster than their sober counterparts. Finally, using this information, for drivers at different BACs, we compared the crash risk attributable to speed with the crash risk as a function of alcohol levels. Accordingly, we estimate that at 0.08 g/dl, higher speeds accounted for nearly 50% of the fatality crash risk attributed to alcohol, and 25% of the fatality crash risk at 0.16 g/dl. For serious injuries, estimates were 39% and 16%, respectively.

CONCLUSIONS: The literature on alcohol-related crashes widely attributes the increased crash risk to impaired driving skills, such as attention, coordination and reaction time. Our analysis suggests that speeding alone might account for some of this elevated risk. This has implications for understanding the etiology of alcohol-related crashes. We also suggest that speed control may be a viable means of reducing the harm from alcohol-related crashes.

PMID:39983026 | DOI:10.1080/15389588.2025.2456942

JMIR Public Health Surveill. 2025 Feb 21;11:e67050. doi: 10.2196/67050.

ABSTRACT

BACKGROUND: The increase in emerging and re-emerging infectious disease outbreaks underscores the need for robust early warning systems (EWSs) to guide mitigation and response measures. Administrative health care databases provide valuable epidemiological insights without imposing additional burdens on health services. However, these datasets are primarily collected for operational use, making data quality assessment essential to ensure an accurate interpretation of epidemiological analysis. This study focuses on the development and implementation of a data quality index (DQI) for surveillance integrated into an EWS for influenza-like illness (ILI) outbreaks using Brazil’s a nationwide Primary Health Care (PHC) dataset.

OBJECTIVE: We aimed to evaluate the impact of data completeness and timeliness on the performance of an EWS for ILI outbreaks and establish optimal thresholds for a suitable DQI, thereby improving the accuracy of outbreak detection and supporting public health surveillance.

METHODS: A composite DQI was established to measure the completeness and timeliness of PHC data from the Brazilian National Information System on Primary Health Care. Completeness was defined as the proportion of weeks within an 8-week rolling window with any register of encounters. Timeliness was calculated as the interval between the date of encounter and its corresponding registry in the information system. The backfilled PHC dataset served as the gold standard to evaluate the impact of varying data quality levels from the weekly updated real-time PHC dataset on the EWS for ILI outbreaks across 5570 Brazilian municipalities from October 10, 2023, to March 10, 2024.

RESULTS: During the study period, the backfilled dataset recorded 198,335,762 ILI-related encounters, averaging 8,623,294 encounters per week. The EWS detected a median of 4 (IQR 2-5) ILI outbreak warnings per municipality using the backfilled dataset. Using the real-time dataset, 12,538 (65%) warnings were concordant with the backfilled dataset. Our analysis revealed that 100% completeness yielded 76.7% concordant warnings, while 80% timeliness resulted in at least 50% concordant warnings. These thresholds were considered optimal for a suitable DQI. Restricting the analysis to municipalities with a suitable DQI increased concordant warnings to 80.4%. A median of 71% (IQR 54%-71.9%) of municipalities met the suitable DQI threshold weekly. Municipalities with ≥60% of weeks achieving a suitable DQI demonstrated the highest concordance between backfilled and real-time datasets, with those achieving ≥80% of weeks showing 82.3% concordance.

CONCLUSIONS: Our findings highlight the critical role of data quality in improving the EWS’ performance based on PHC data for detecting ILI outbreaks. The proposed framework for real-time DQI monitoring is a practical approach and can be adapted to other surveillance systems, providing insights for similar implementations. We demonstrate that optimal completeness and timeliness of data significantly impact the EWS’ ability to detect ILI outbreaks. Continuous monitoring and improvement of data quality should remain a priority to strengthen the reliability and effectiveness of surveillance systems.

PMID:39983017 | DOI:10.2196/67050

Genome Biol Evol. 2025 Feb 3;17(2):evae275. doi: 10.1093/gbe/evae275.

ABSTRACT

Evolution by natural selection occurs at its most basic through the change in frequencies of alleles; connecting those genomic targets to phenotypic selection is an important goal for evolutionary biology in the genomics era. The relative abundance of gene products expressed in a tissue can be considered a phenotype intermediate to the genes and genomic regulatory elements themselves and more traditionally measured macroscopic phenotypic traits such as flowering time, size, or growth. The high dimensionality, low sample size nature of transcriptomic sequence data is a double-edged sword, however, as it provides abundant information but makes traditional statistics difficult. Machine learning (ML) has many features which handle high-dimensional data well and is thus useful in genetic sequence applications. Here, we examined the association of fitness components with gene expression data in Ipomoea hederacea (Ivyleaf morning glory) grown under field conditions. We combine the results of two different ML approaches and find evidence that expression of photosynthesis-related genes is likely under selection. We also find that genes related to stress and light responses were overall important in predicting fitness. With this study, we demonstrate the utility of ML models for smaller samples and their potential application for understanding natural selection.

PMID:39983007 | DOI:10.1093/gbe/evae275