Victoria Aranda

1 week ago | nature.com | Jung Hun Kang |Jun Lee |Dong-gi Lee |Jae Choi |Jun Baek Park |Yeasol Kwon | +2 more

We aimed to evaluate the relationship between serum testosterone levels and kidney stone prevalence in men. We examined cross-sectional data from 3234 men who participated in a health examination (2010–2020). A full metabolic work-up, including serum testosterone levels, was performed. Combined ultrasonography with KUB radiography was used for stone detection. The participants’ median age and testosterone concentration were 53.0 years and 4.7 ng/mL, respectively. A total of 178 men had kidney stones. A cutoff value for determining the presence of kidney stones was a testosterone concentration <3.33 ng/mL ng/mL. After adjusting for confounders, only age and a testosterone concentration <3.33 ng/mL were significantly related to the presence of kidney stones. However, body mass index, blood pressure, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, creatinine, blood urea nitrogen, HbA1c, uric acid, hs-CRP, calcium, aspartate transaminase, alanine aminotransferase, and albumin were not significantly and independently related to kidney stones. The odds ratios (95% confidence intervals) for kidney stones according to age and testosterone concentration <3.33 ng/mL were 1.029 (1.010–1.04) and 1.655 (1.071–2.556), respectively. Our study revealed that the prevalence of kidney stones significantly and independently increased when the serum testosterone was less than 3.33 ng/mL in men.

2 months ago | nature.com | Aditya R. Pote |Kamal Kumar |Dhruv Kumar |Vikash Kumar |Jagat Sesh Challa |Piyush Jhamnani | +3 more

This study evaluates the effectiveness of three leading generative AI tools-ChatGPT, Gemini, and Copilot-in undergraduate mechanical engineering education using a mixed-methods approach. The performance of these tools was assessed on 800 questions spanning seven core subjects, covering multiple-choice, numerical, and theory-based formats. While all three AI tools demonstrated strong performance in theory-based questions, they struggled with numerical problem-solving, particularly in areas requiring deep conceptual understanding and complex calculations. Among them, Copilot achieved the highest accuracy (60.38%), followed by Gemini (57.13%) and ChatGPT (46.63%). To complement these findings, a survey of 172 students and interviews with 20 participants provided insights into user experiences, challenges, and perceptions of AI in academic settings. Thematic analysis revealed concerns regarding AI’s reliability in numerical tasks and its potential impact on students’ problem-solving abilities. Based on these results, this study offers strategic recommendations for integrating AI into mechanical engineering curricula, ensuring its responsible use to enhance learning without fostering dependency. Additionally, we propose instructional strategies to help educators adapt assessment methods in the era of AI-assisted learning. These findings contribute to the broader discussion on AI’s role in engineering education and its implications for future learning methodologies.

Feb 5, 2025 | nature.com | Boadie W Dunlop |Charles Nemeroff |Helen S. Mayberg |W. Edward Craighead |Victoria Aranda

Treatments for major depressive disorder (MDD) include antidepressant medications and evidence-based psychotherapies, which are approximately equally efficacious. Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differential prediction of treatment response and personalized treatment recommendation. We used machine learning to develop predictor variables that combined demographic and clinical items from a randomized clinical trial. The variables predicted a meaningful proportion of variance in end-of-treatment depression severity for cognitive behavioral therapy (39.7%), escitalopram (32.1%), and duloxetine (67.7%), leading to a high accuracy in predicting remission (71%). Further, we used these variables to simulate treatment recommendation and found that patients who received their recommended treatment had significantly improved depression severity and remission likelihood. Finally, the prediction algorithms and treatment recommendation tool were externally validated in an independent sample. These results represent a highly promising, easily implemented, potential advance for personalized medicine in MDD treatment.

Nov 4, 2024 | nature.com | Xiaobing Zhai |Abao Xing |Gang Luo |Junfeng Li |Miao Zhou |Cuicui Wang | +7 more

Depression represents a significant global public health challenge, and marital status has been recognized as a potential risk factor. However, previous investigations of this association have primarily focused on Western samples with substantial heterogeneity. Our study aimed to examine the association between marital status and depressive symptoms across countries with diverse cultural backgrounds using a large-scale, two-stage, cross-country analysis. We used nationally representative, de-identified individual-level data from seven countries, including the USA, the UK, Mexico, Ireland, Korea, China and Indonesia (106,556 cross-sectional and 20,865 longitudinal participants), representing approximately 541 million adults. The follow-up duration ranged from 4 to 18 years. Our analysis revealed that unmarried individuals had a higher risk of depressive symptoms than their married counterparts across all countries (pooled odds ratio, 1.86; 95% confidence interval (CI), 1.61–2.14). However, the magnitude of this risk was influenced by country, sex and education level, with greater risk in Western versus Eastern countries (β = 0.36; 95% CI, 0.16–0.56; P < 0.001), among males versus females (β = 0.25; 95% CI, 0.003–0.47; P = 0.047) and among those with higher versus lower educational attainment (β2 = 0.34; 95% CI, 0.11–0.56; P = 0.003). Furthermore, alcohol drinking causally mediated increased later depressive symptom risk among widowed, divorced/separated and single Chinese, Korean and Mexican participants (all P < 0.001). Similarly, smoking was as identified as a causal mediator among single individuals in China and Mexico, and the results remained unchanged in the bootstrap resampling validation and the sensitivity analyses. Our cross-country analysis suggests that unmarried individuals may be at greater risk of depression, and any efforts to mitigate this risk should consider the roles of cultural context, sex, educational attainment and substance use. Analysing data from seven countries, this study found that unmarried individuals had a higher depression risk than married individuals. This risk was higher in Western countries, among males and among those with higher educational attainment.

Oct 28, 2024 | nature.com | David Benrimoh |Igor D. Bandeira |Ian H. Kratter |Nolan Williams |Xiaoqian Xiao |Victoria Aranda | +4 more

Stanford Neuromodulation Therapy (SNT), has recently shown rapid efficacy in difficult to treat (DTT) depression. We conducted an exploratory analysis of individual symptom improvements during treatment, correlated with fMRI, to investigate this rapid improvement in 23 DTT participants from an SNT RCT (12 active, 11 sham). Montgomery–Åsberg Depression Rating Scale item 7 (Lassitude) was the earliest to show improvements between active and sham, as early as treatment day 2. Lassitude score at treatment day 3 was predictive of response at 4 weeks post-treatment and response immediately after treatment. Participants with lower lassitude scores at treatment day 3 had different patterns of sgACC functional connectivity compared to participants with higher scores in both baseline and post-treatment minus baseline analyses. Further work will aim to first replicate these preliminary findings, and then to extend these findings and examine how SNT may affect lassitude and behavioral activation early in treatment.