Nevin Manimala

J Ovarian Res. 2026 Feb 7. doi: 10.1186/s13048-025-01901-1. Online ahead of print.

ABSTRACT

BACKGROUND: Germline mutations of BRCA1 and BRCA2 may impair DNA repair in the ovarian cortex, leading to increased oocyte apoptosis, thus, affecting ovarian reserve. Aim of this study was to assess follicular density in ovarian biopsies from women with breast cancer carrying BRCA1 and BRCA2 mutations who underwent ovarian tissue cryopreservation (OTC) at our center.

METHODS: This was a single center, observational, cross-sectional study carried out in a tertiary level referral center for fertility preservation treatment. Exclusion criteria were: patients aged < 18 years or > 38 years, patients who had already undergone chemotherapy/pelvic radiotherapy at the time of OTC, patients without data on follicular density and those with unknown BRCA mutational status. Follicular density was defined as the number of primordial, intermediate primordial, small primary, large primary, secondary, preantral, and antral follicles per 1 mm² of cortical section area.

RESULTS: Out of 216 patients, 21 women reported germline mutation: 9 (4.2%) were carriers of the BRCA1 mutation and 13 (6%) of the BRCA2 mutation. The mean age at OTC was 31.5 ± 3.6 years, and the median age was 32.4 years (range, 21-38). No significant difference in follicular density was observed among women without BRCA mutations, those with BRCA1 mutations, and those with BRCA2 mutations. The median follicular density was 4.0/mm² (range 0-74.5) in BRCA-negative women, 3.5/mm² (range 0-20) in women with BRCA1 mutations, and 4.0/mm² (range 0-32) in women with BRCA2 mutations (p = 0.272 and p = 0.703, respectively). After adjusting for age, no statistically significant differences in follicular density were observed according to BRCA1 and BRCA2 mutation status: the median follicular density was 4.6/mm² in BRCA-negative women, 3.1/mm² in women with BRCA1 mutations, and 3.6/mm² in women with BRCA2 mutations (p = 0.428 and p = 0.385, respectively).

CONCLUSIONS: No significant difference in follicular density was observed between women with BRCA1/BRCA2 mutations and those without. Our findings suggest that the presence of a BRCA mutation does not have a significant negative clinical impact on the follicular population of the ovarian cortex. Larger studies are needed to further validate these findings.

PMID:41654853 | DOI:10.1186/s13048-025-01901-1

BMC Complement Med Ther. 2026 Feb 7. doi: 10.1186/s12906-026-05289-3. Online ahead of print.

NO ABSTRACT

PMID:41654841 | DOI:10.1186/s12906-026-05289-3

BMC Public Health. 2026 Feb 7. doi: 10.1186/s12889-026-26526-x. Online ahead of print.

ABSTRACT

BACKGROUND: Academic performance is often highly prioritized among college students, sometimes at the expense of their health. Despite growing interest in this relationship, limited research with college students has explored how diet quality (DQ) varies by gender, first-generation status, and grade-point average (GPA). The purpose of this paper was to: (1) examine the relationship between DQ and academic performance in college students and (2) identify potential differences based on gender, first-generation status, and varying GPAs.

METHODS: In this cross-sectional study, undergraduate students (n = 301), mean age 21.2 (SD ± 2.49), completed the validated Short Healthy Eating Index (sHEI) based on the USDA’s Healthy Eating Index (HEI) per 2015-2020 Dietary Guidelines for Americans, to examine DQ. Academic performance was assessed using self-reported GPA. Students were predominantly non-Hispanic White (63%), Female (61%), and 75% had at least one parent graduate college. Descriptive statistics, correlation, and one-way ANOVAs were used to analyze the data using SPSS V.29. GPA was categorized into 3 groups: high, mid, and low GPA groups. Results were significant when p < 0.05.

RESULTS: DQ scores ranged from 21% to 68%, with a mean of 44% (SD: ±2.494). There were no significant associations between GPA and total DQ. However, significant associations were found between gender and specific dietary components. Further, total protein scores were greater among students with a high GPA compared to low and mid-GPA groups (F = 5.214, p = 0.006). Plant-based protein was greater among students who had at least one parent graduate college compared to first-generation students (F = 3.435, p = 0.034). Students living independently had lower total protein scores compared to those living with family (F = 4.841, p = 0.029). Additionally, students without a current job had higher dairy scores than those employed (F = 4.280, p = 0.039).

CONCLUSION: Overall, college students reported poor DQ; however, personal (e.g., gender) and environmental factors (e.g., living arrangements) were associated with one’s DQ. Further investigation is needed to facilitate the development of effective interventions that encourage healthier dietary habits among college students to improve their overall health and wellness.

PMID:41654838 | DOI:10.1186/s12889-026-26526-x

J Orthop Surg Res. 2026 Feb 7. doi: 10.1186/s13018-026-06727-1. Online ahead of print.

ABSTRACT

PURPOSE: To explore the clinical significance of the artificial intelligence (AI)-assisted three-dimensional (3D) planning system AI-HIP in total hip arthroplasty (THA) and evaluate its accuracy and efficacy in clinical practice.

METHODS: Preoperative planning was done using the AI-HIP system in the AI group and two-dimensional (2D) template measurements in the conventional group. The two groups were compared for postoperative radiographic results, perioperative monitoring indicators, and the degree of consistency between preoperative planning and actual implant size. Postoperative Harris scores, hip joint range of motion (ROM), and Barthel index were used to evaluate clinical effectiveness.

RESULTS: None of the patients who ultimately completed the 6-months follow-up experienced adverse events such as hip dislocation and infection during follow-up. Compared to the conventional group, the AI group had significantly higher Harris scores (P = 0.026), hip ROM (P = 0.018), Barthel index (P = 0.042) at 6 months postoperatively, and conformity rates of the acetabular (P = 0.001) and femoral components (P < 0.001) between intraoperative application of prosthesis model and preoperative planning. Additionally, the AI group had significantly shorter operation time (P = 0.041), less intraoperative blood loss (P = 0.012), and smaller discrepancy between bilateral acetabular offset (P = 0.032) and vertical distance of hip center of rotation (P = 0.011). However, no statistically significant intergroup differences were observed for the acetabular abduction angle, anteversion angle, femoral offset and leg length discrepancy.

CONCLUSION: Preoperative planning for THA using the AI-HIP system has a high accuracy rate and allows for effective reconstruction of the rotation center and acetabular offset, reduction of surgical time, and early recovery of joint function. Further research is needed to confirm its potential clinical value.

CLINICAL REGISTRATION NUMBER: ChiCTR210004826, Date:28/03/2021, https://www.chictr.org.cn/showproj.html? proj=52846.

PMID:41654837 | DOI:10.1186/s13018-026-06727-1

BMC Oral Health. 2026 Feb 7. doi: 10.1186/s12903-026-07727-7. Online ahead of print.

ABSTRACT

OBJECTIVE: This study evaluated multiple deep learning approaches for automated classification of dental conditions in panoramic radiographs, comparing custom convolutional neural networks (CNNs), hybrid CNN-machine learning models, and fine-tuned pre-trained architectures, comparing the performance of custom convolutional neural networks (CNNs), hybrid CNN-machine learning models, and fine-tuned pre-trained architectures for detecting fillings, cavities, implants, and impacted teeth.

METHODS: A dataset of 1,512 panoramic X-ray images with 11,137 manually annotated bounding boxes for four dental conditions (fillings, cavities, implants, and impacted teeth) was analyzed, with regions of interest extracted using expert annotations for subsequent AI-based classification. Class imbalance was addressed through random downsampling, creating a balanced dataset of 894 samples per condition. Multiple approaches were evaluated via 5-fold cross-validation: a custom CNN, hybrid models combining CNN features with traditional classifiers (Support Vector Machine, Decision Tree, Random Forest), and fine-tuned pre-trained networks (VGG16, Xception, ResNet50). Performance was assessed using accuracy, precision, recall, and F1-score metrics.

RESULTS: The hybrid CNN-Random Forest model achieved the highest accuracy of 85.4 ± 2.3% with macro-F1 score of 0.843 ± 0.028, representing an 11% point improvement over the custom CNN (74.29% accuracy, 0.724 macro-F1). VGG16 demonstrated superior pre-trained architecture performance (82.3 ± 2.0% accuracy, 0.817 macro-F1), followed by Xception (80.9 ± 2.3%) and ResNet50 (79.5 ± 2.7%). CNN + Random Forest exhibited exceptional fillings detection (F1: 0.860 ± 0.033) with balanced multi-class performance. Systematic misclassifications between morphologically similar conditions revealed inherent diagnostic challenges.

CONCLUSION: Hybrid CNN-based approaches combining feature extraction with Random Forest classification provide superior discriminative capability for dental condition detection on manually annotated regions compared to standalone architectures. While computationally efficient hybrid models show promise as supportive diagnostic tools, observed misclassification patterns indicate these AI systems should serve as adjuncts to clinical expertise, requiring prospective validation studies.

PMID:41654817 | DOI:10.1186/s12903-026-07727-7

BMC Public Health. 2026 Feb 7. doi: 10.1186/s12889-026-26510-5. Online ahead of print.

ABSTRACT

BACKGROUND: Although many studies have investigated COVID-19 outbreaks in long-term care facilities (LTCFs), evidence that combines multiple clustered levels is scarce. We aimed to describe individual, LTCF, and regional-level factors associated with COVID-19 infections.

METHODS: We conducted a nationwide study using insurance claims data from Germany between 1st October 2020 and 31st March 2021. The sample comprised 284,186 residents over 60 years in 9,869 LTCFs across all of Germany’s 400 districts. We used multilevel logistic regression to model associations between individual, LTCF, and district-level factors, and the probability of a COVID-19 infection.

RESULTS: A total of 44,042 (15.5%) COVID-19 infections were recorded during the study period. On the individual level, male sex (OR 1.15; 95% CI 1.12-1.18), dementia (OR 1.09; CI 1.06-1.11), medium-severe care dependency level 3 and 4 (OR 1.17; CI 1.12-1.22 / OR 1.21; CI 1.16-1.26) were associated with greater risk of infection. At the LTCF level, infection risks increased with the mean age of residents (OR 1.09; CI 1.03-1.15) and higher resident numbers (OR 1.20; CI 1.14-1.27). On the district level, a higher proportion of public LTCFs was associated with lower infection risks (OR 0.90; CI 0.84-0.97), while a higher mean number of residents (OR 1.16; CI 1.05-1.28), and the district-level SARS-CoV-2 incidence rate among the general population (OR 1.54; CI 1.41-1.67) was associated with higher risks. A cross-level interaction between facility size and COVID-19 prevalence was not significant (p > 0.5).

CONCLUSION: We found evidence of individual, facility, and regional levels factors associated with COVID-19 infections among older adults in LTCFs. Future measures to combat infections, outbreaks, and pandemics should take an orchestrated multilevel approach.

PMID:41654798 | DOI:10.1186/s12889-026-26510-5

BMC Public Health. 2026 Feb 7. doi: 10.1186/s12889-026-26323-6. Online ahead of print.

NO ABSTRACT

PMID:41654778 | DOI:10.1186/s12889-026-26323-6

J Periodontol. 2026 Feb 7. doi: 10.1002/jper.70072. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to perform a prediction model validation for furcation involvement (FI) risk in molars receiving a new fixed prosthesis (FP) using a unique cohort assessed at three time points.

METHODS: Following the Oral Health Statistical (OHStat) reporting guidelines, this cohort study examined 181 patients (203 molars) from 2018-2023. Teeth without FI were followed longitudinally post-crown placement at 1- (T1), 3- (T2), and 5-years (T3). A logistic regression model was built in order to predict FI and the related performance was assessed through metrics like AUC, sensitivity, specificity, and calibration.

RESULTS: FI was observed in 4.43% of teeth at 1 year, increasing to 21.67% at 3 years and 28.57% at 5 years. Univariate analysis revealed significant predictors at 3-5 years: a history of periodontitis was associated with higher FI risk at 5 years (RR = 3.56, p = 0.024), with advanced stages also increasing risk-stage III: RR = 2.59 at 3 years and RR = 3.32 at 5 years; stage IV: RR = 3.76 at 3 years and RR = 3.75 at 5 years. Short root trunks significantly increased FI risk across all intervals (1 year: RR = 3.96; 3 years: RR = 6.08; 5 years: RR = 4.75). Medium trunks did not differ significantly from long trunks. The predictive model performed best at 3 years (AUC = 0.81, sensitivity = 0.79, specificity = 0.87) and remained robust at 5 years (AUC = 0.76, sensitivity = 0.69, specificity = 0.90).

CONCLUSION: The predictive model demonstrated high accuracy with a substantial ability to identify FI cases over time. Clinicians should consider such an assessment before crown or bridge restoration, with particular caution in patients with periodontitis.

PMID:41653409 | DOI:10.1002/jper.70072

Aging Clin Exp Res. 2026 Feb 7. doi: 10.1007/s40520-026-03336-0. Online ahead of print.

ABSTRACT

BACKGROUND: Low back pain (LBP) is a global health problem that contributes to disability, psychological distress, and reduced quality of life in older adults. Current treatment guidelines for LBP support the use of conservative therapies such as physical therapy alongside medication management. However, interventional pain management strategies for LBP such as lumbar epidural steroid injection (LESI) are rarely mentioned.

AIMS: We conducted this systematic review to characterize and evaluate the use and effectiveness of LESI amongst older adults with LBP.

METHODS: We conducted a systematic English-language literature search of Ovid MEDLINE, Ovid EMBASE, and Cochrane Library. We used an iterative approach to identify both keywords and controlled vocabulary related to treatment outcomes of epidural interventions for LBP among older adults. The results were reviewed by three members of the team.

RESULTS: Our search of 3 databases produced a total of 2657 studies and 12 met final inclusion criteria. In all studies, the use of LESI was associated with improvement in pain and/or quality of life.

DISCUSSION: As compared to medication management, LESI was associated with statistically significant improvements in pain and functional status. The addition of physical therapy was not superior to LESI alone.

CONCLUSIONS: This systematic review is the first that focuses on the effectiveness of LESI in managing LBP in older adults. This review suggests that LESI may reduce pain and improve functional status in older adults, particularly as compared to medication management.

PROSPERO REGISTRATION: The study was prospectively registered on Prospero (ID # 422087).

CLINICAL TRIAL NUMBER: Not Applicable.

PMID:41653389 | DOI:10.1007/s40520-026-03336-0

Clin Exp Med. 2026 Feb 7. doi: 10.1007/s10238-026-02065-y. Online ahead of print.

NO ABSTRACT

PMID:41653319 | DOI:10.1007/s10238-026-02065-y