This study examined the association between adverse childhood experiences (ACEs), including discrimination due to sexual orientation and gender identity, and exposure to secondhand smoke (SHS). Using data from the 2018–2020 National Survey of Children’s Health (NSCH), logistic regression models calculated adjusted odds ratios (AORs) of two SHS measures: (1) anyone living in the household uses combustible tobacco products, irrespective of them using that product in the home or not, and (2) anyone smoking inside the home. Individual and cumulative ACEs served as independent variables. Data from the 2020 NSCH included discrimination due to sexual orientation and gender identity as an ACE, for children 6–17, but prior years did not. Each additional ACE was associated with a 36
PURPOSE:The Medtronic implantable tibial neuromodulation system is a leadless, rechargeable, minimally invasive option intended to treat the symptoms of urge urinary incontinence (UUI), a common condition that significantly impairs quality of life. TITAN 2 is a pivotal study evaluating safety and effectiveness of the Medtronic implantable tibial neuromodulation system. The results through 12 months are reported. MATERIALS AND METHODS:This prospective, multicenter, single-arm investigational device exemption study enrolled eligible overactive bladder subjects with UUI. The primary end point was the percentage of subjects who had ≥ 50% reduction in UUI episodes/day after 6 months. Safety was characterized as the rate of adverse device effects (ADEs). Data are reported as mean ± SD or with 95% CIs. RESULTS:Of 126 implanted subjects, 95% were female, the mean age was 63 ± 14 years, and BMI was 35 ± 9 kg/m2. The primary objective was met with 59% of subjects achieving ≥ 50% improvement in UUI episodes/day (95% CI: 49%-67%, P < .0001) at 6 months; at 12 months, 61% had ≥ 50% improvement. Change from baseline was statistically significant at 6 months for UUI episodes/day, voids/day (in urinary frequency subjects), and Urgency Perception Score, with improvements in all 3 symptoms maintained through 12 months. Twenty percent of subjects experienced ADEs through 12 months; 1 ADE was serious and was recovered/resolved. CONCLUSIONS:TITAN 2 met the primary and all secondary objectives by showing a statistically significant improvement of UUI, urinary frequency, and urgency symptoms. Clinical benefits and a favorable safety profile were maintained through 12 months.
Background: Although pancreatic cancer is an aggressive malignancy with a propensity for metastatic spread, brain metastases (BrMs) remain exceptionally rare (<1% incidence). Case Description: We present a case of BrMs in a 69-year-old pancreatic cancer patient. This patient initially presented with a history of chronic pancreatitis, abdominal and back pain, and significant weight loss. After the diagnostic biopsy confirmed pancreatic cancer, the patient was enrolled onto the PASS-01 clinical trial and was treated with standard of care chemotherapy before progressing and developing BrMs 10 months after cancer diagnosis. The BrMs were removed via craniotomy, and the patient also underwent radiation and chemotherapy before enrolling onto the MYTHIC clinical trial for PKMYT1 inhibitor, RP-6306. Successful generation of a BrM-derived organoid line enabled high-throughput drug screening, which recapitulated the patient's clinical resistance to standard therapies [gemcitabine/nab-paclitaxel, 5-fluorouracil (5-FU)], and showed sensitivity to afatinib, everolimus and RMC-6236. Conclusions: This case report demonstrates the importance of precision medicine to characterize patient tumor and identify actionable targets and potential therapies. Physicians should also be aware that KRAS wild-type pancreatic cancer with atypical amplifications is likely to exhibit unique metastatic tropisms. Finally, we suggest that patient-derived organoids pharmacotyping can mirror clinical drug responses and help evaluate therapeutic avenues for patient treatment.
PURPOSE:Distinguishing high-risk intraductal papillary mucinous neoplasms (IPMNs) from low-risk lesions remains a clinical challenge, often resulting in unnecessary procedures due to limited specificity of current methods. While radiomics and deep learning (DL) have been explored for pancreatic cancer, cyst-level malignancy risk stratification of IPMNs remains untapped. METHODS:Our multi-institutional assessed the feasibility of AI for predicting IPMN dysplasia grade using cyst-level image features using 359 T2-weighted (T2W) MRI images from seven centers. We developed and compared 2D and 3D radiomics-only, DL-only, and radiomics-DL fusion models using expert radiologist scoring as a baseline reference. Model performance was evaluated using held-out test data. RESULTS:The radiomics-DL fusion model showed the highest discriminatory ability on the test set AUC of 69.2%, outperforming the radiomics-only model, AUC of 66.5%. Expert accuracy varied widely from 37.4% to 66.7%, and the inter-rater agreement varied as well with weighted Cohen's kappa coefficients of 0.33-0.67. CONCLUSION:The fusion model, which combines DL with radiomics features from routine T2W MRI, shows promise for objective, cyst-level risk stratification of IPMNs in a multi-center cohort, outperforming radiomics-only models and nearly matching expert radiologists using only T2W and T1-weighted (T1W) sequences. While performance requires improvement for standalone clinical use, this approach offers a scalable, non-invasive method to potentially improve diagnostic accuracy and reduce unnecessary surgical interventions.
Background:Organ recovery for transplantation costs approximately $40,000 per donor, yet nearly 20% of recovered kidneys ultimately go unused. Static viability scores (e.g., KDPI, DRI) provide baseline risk stratification but fail to capture dynamic physiological changes that occur during donor management. Methods:We trained and evaluated six machine learning (ML) models using the ORCHID dataset derived from 56 U.S. organ procurement organizations (OPOs) between 2015 and 2021. To ensure generalizability, we implemented a spatial hold-out validation strategy, training models on 70% of OPOs and testing them on a geographically distinct 30% cohort. Predictors included prerecovery vital signs, blood chemistry, serologic markers, donor demographics, and circumstances of death. We compared logistic regression (linear baseline), random forest, XGBoost, LightGBM, CatBoost, and a multilayer perceptron (NN-MLP). Hyperparameters were optimized using Bayesian methods, and missing values were addressed using median imputation to prevent zero-inflation bias. Results:Nonlinear, tree-based models significantly outperformed both the linear baseline and KDPI. LightGBM achieved the highest discrimination (ROCAUC, 0.975; PRAUC, 0.809), followed closely by random forest (ROCAUC, 0.975) and CatBoost (ROCAUC, 0.974). At a classification threshold of 0.5, the top-performing model demonstrated 94.1% accuracy and 88.1% sensitivity. In contrast, logistic regression showed lower sensitivity (77.5%), suggesting that ML approaches more effectively identify borderline viable organs. The most influential predictors included serum creatinine, brain death status, blood urea nitrogen, and donor age. Compared with KDPI alone (ROCAUC, 0.830), the ML model improved discrimination by more than 0.14. Conclusions:A dynamic ML model evaluated using spatial validation substantially outperforms static scoring systems for predicting kidney utilization. By providing interpretable, near-real-time assessments, this approach could help OPOs avoid failed recoveries and reduce unnecessary organ discards. Prospective trials are needed to assess its impact on downstream clinical transplant outcomes.
