medRxiv最新文献

{"title":"Adult renal tubular organoids can be produced from different human individuals in a completely same protocol","authors":"M. Mori, Y. Mori, Nakao Yuki, Shintaro Mandai, T. Fujiki, Hiroaki Kikuchi, Fumiaki Ando, K. Susa, Takayasu Mori, Y. Waseda, S. Yoshida, Y. Fujii, E. Sohara, Shinichi Uchida","doi":"10.1101/2024.08.11.24311846","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311846","url":null,"abstract":"Introduction: Organoids are miniature organs produced by newly emerging technologies. Kidney organoids originated from human inducible pluripotent stem cells (iPSCs) were developed to recapitulate renal diseases. However, producing iPSC kidney organoids from multiple individuals at the same time and in a uniform condition is still impossible. Here, we report adult renal tubular organoids, \"tubuloids\", established from primary renal epithelial cells from multiple human individuals in a uniform manner. Methods: Kidneys obtained from patients due to the surgery for malignancy were minced into small pieces, and primary renal epithelial tubule cells are cultured. 4 patients had normal kidney function and 4 had mild chronic kidney disease (CKD). Growth factors were added to the primary cultured cells at the same time and Matrigel was added to these 8 lines. Results: Primary cultured renal epithelial cells from normal kidneys showed a large number of fine, swollen epithelial appearance. On the other hand, primary cultured kidney epithelial cells from mild CKD kidneys were smaller and slightly elongated than those of normal kidneys. The growth speed was faster in normal kidney cells than in mild CKD cells. At the beginning of the three-dimensionalization (day 0), normal renal tubuloids grew faster than mild CKD tubuloids. The difference in size between normal tubuloids and mild CKD ones became less noticeable on day 5. Both types of tubuloids reached almost same size on day 10. All 8 strains are of different human origin, and uniform tubuloids could be produced at the same time and in a uniform protocol. Conclusion: In terms of pathological models, the differences between mouse models and humans cannot be ignored, and there is a great need for a more human-like model of human pathology from both medical and research perspectives. Our renal tubular organoids can be produced in a uniform manner at the same time. It is expected to be used as a new type of convenient human pathological model.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide association study of COVID-19 Breakthrough Infections and genetic overlap with other diseases: A study of the UK Biobank","authors":"Yaning Feng, Kenneth Chi-Yin, Wong, Wai Kai Tsui, Ruoyu Zhang, Yong XIANG, SO Hon-Cheong, Lo","doi":"10.1101/2024.08.11.24311845","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311845","url":null,"abstract":"Background: The COVID-19 pandemic has led to substantial health and financial burden worldwide, and vaccines provide hope to reduce the burden of this pandemic. However, vaccinated people remain at risk for SARS-CoV-2 infection. Genome-wide association studies (GWAS) may allow for the identification of potential genetic factors involved in the development of COVID-19 breakthrough infections (BI), however very few or no GWAS have been conducted for COVID-19 BI so far. Methods: We conducted a GWAS and detailed bioinformatics analysis on COVID-19 BI in a European population based on the UK-Biobank (UKBB). We conducted a series of analyses at different levels, including SNP-based, gene-based, pathway, and transcriptome-wide association analyses, to investigate genetic factors associated with COVID-19 BI and hospitalized infection. Polygenic risk score (PRS) and Hoeffding's test were performed to reveal genetic relationships between BI and other medical conditions. Results: Two independent loci (LD-clumped at r2=0.01) reached genome-wide significance (p<5e-08), including rs36170929 mapped to LOC102725191/VWDE, and rs28645263 mapped to RETREG1. Pathway enrichment analysis highlighted pathways such as viral myocarditis, Rho-selective guanine exchange factor AKAP13 signaling, and lipid metabolism. PRS analyses showed significant genetic overlap between COVID-19 BI and heart failure, HbA1c and type 1 diabetes. Genetic dependence was also observed between COVID-19 BI and asthma, lung abnormalities, schizophrenia, and type 1 diabetes, based on the Hoeffding's test. Conclusions: This GWAS study revealed two significant loci that may be associated with COVID-19 BI, and a number of genes and pathways that may be involved in BI. Genetic overlap with other diseases was identified. Further studies are warranted to replicate the findings and elucidate the mechanisms involved.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"19 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The accuracy of large language models in labelling neurosurgical 'case-control studies and risk of bias assessment: protocol for a study of interrater agreement with human reviewers.","authors":"Joanne Igoli, Temidayo Osunronbi, O. Olukoya, Jeremiah Oluwatomi, Itodo Daniel, Hillary O. Alemenzohu, Alieu Kanu, Alex Mwangi Kihunyu, Ebuka Okeleke, Henry Oyoyo, Oluwatobi Shekoni, D. Jesuyajolu, Andrew F Alalade","doi":"10.1101/2024.08.11.24311830","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311830","url":null,"abstract":"Introduction: Accurate identification of study designs and risk of bias (RoB) assessment is crucial for evidence synthesis in research. However, mislabelling of case-control studies (CCS) is prevalent, leading to a downgraded quality of evidence. Large Language Models (LLMs), a form of artificial intelligence, have shown impressive performance in various medical tasks. Still, their utility and application in categorising study designs and assessing RoB needs to be further explored. This study will evaluate the performance of four publicly available LLMs (ChatGPT-3.5, ChatGPT-4, Claude 3 Sonnet, Claude 3 Opus) in accurately identifying CCS designs from the neurosurgical literature. Secondly, we will assess the human-LLM interrater agreement for RoB assessment of true CCS. Methods: We identified thirty-four top-ranking neurosurgical-focused journals and searched them on PubMed/MEDLINE for manuscripts reported as CCS in the title/abstract. Human reviewers will independently assess study designs and RoB using the Newcastle-Ottawa Scale. The methods sections/full-text articles will be provided to LLMs to determine study designs and assess RoB. Cohen's kappa will be used to evaluate human-human, human-LLM and LLM-LLM interrater agreement. Logistic regression will be used to assess study characteristics affecting performance. A p-value < 0.05 at a 95% confidence interval will be considered statistically significant. Conclusion If the human-LLM agreement is high, LLMs could become valuable teaching and quality assurance tools for critical appraisal in neurosurgery and other medical fields. This study will contribute to validating LLMs for specialised scientific tasks in evidence synthesis. This could lead to reduced review costs, faster completion, standardisation, and minimal errors in evidence synthesis.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Population-level Accessibility to Medical College Hospitals in India: A Geospatial Modeling Study","authors":"H. Thakkar, C. Reddy, V. R. Passi, A. Miyajiwala, S. Kale, A. Raj, S. Zadey","doi":"10.1101/2024.08.11.24311839","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311839","url":null,"abstract":"Background: While studies have investigated the availability of Medical College Hospitals (MCHs) in India, data on geographical accessibility is limited. Our study looks at the current geographical accessibility to these MCHs across 36 states and union territories (UTs) and 735 districts. Methods and Findings: We provided and validated the MCH data acquired from the National Health Profile Report 2022. We took motorized and walking travel-time friction surface rasters from the Malaria Atlas Project 2019 and high-resolution population estimates from WorldPop 2020. Using these, we examined the density of MCHs per million population and the median travel time to the nearest MCH. We assessed the Access Population Coverage (APC), defined as the proportion of the population within 30, 60, 90, and 120 minutes by motorized transport and within 30 and 60 minutes from the nearest MCH by walking. In 2022, India had an average density of 0.47 MCHs per million. The median travel time to the nearest MCH was 67.94 minutes by motorized transport and 589.82 minutes by walking. 71.76% of the population could access the nearest MCH by motorized transport within 60 minutes (range across districts: 0-100%). 4.22% of the population could access the nearest MCH by walking within 30 minutes (range across districts: 0-71.86%). The APC was 62.20% within 60 minutes by motorized transport in rural vs. 92.34% in urban areas. The APC within 60 minutes by motorized transport for public MCHs was 63.62%, while that for private was 45.95%. These estimates do not account for resource availability at the hospitals or vehicular ownership in the population. Conclusions: Median travel time and APC are useful for assessing geographical accessibility. Our study found a wide disparity in MCH access across Indian states and rural vs. urban areas. These analyses can guide the optimal placement of new MCHs.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lack of Epistatic Interaction of SNCA with APOE in Synucleinopathies","authors":"P. Saini, E. Yu, M. Estiar, L. Krohn, Kheireddin Mufti, Uladzislau Rudakou, J. Ruskey, F. Asayesh, S. Laurent, D. Spiegelman, J. Trempe, T. Quinnell, Nicholas Oscroft, Isabelle Arnulf, J. Montplaisir, J. Gagnon, A. Desautels, Y. Dauvilliers, Gian Luigi Gigli, M. Valente, Francesco Janes, A. Bernardini, K. Šonka, D. Kemlink, Wolfgang Oertel, Karri Kaivola, International Lbd Genomics Consortium, A. Janzen, G. Plazzi, E. Antelmi, F. Biscarini, M. Figorilli, M. Puligheddu, B. Mollenhauer, C. Trenkwalder, F. Sixel-Döring, V. C. Cock, C. Monaca, Donald G. Grosset, A. Heidbreder, Luigi Ferini-Strambi, F. Dijkstra, M. Viaene, B. Abril, B. Boeve, R. Postuma, Guy A. Rouleau, Victoria Anselmi, Abubaker Ibrahim, A. Stefani, Birgit Högl, Michele T.M. Hu, Sonja W Scholz, Z. Gan-Or, Montreal Neurological, Institute","doi":"10.1101/2024.08.12.24311821","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311821","url":null,"abstract":"Two recent studies suggested that the APOE {varepsilon}4 haplotype was associated with increased -synuclein pathology in cell and mouse models. Genetic variants in the SNCA region have strong association with Parkinson's disease (PD), Dementia with Lewy Bodies (DLB), and idiopathic REM Sleep Behavior Disorder (iRBD), while APOE is a genetic risk determinant for only DLB. To determine if genetic-level interactions between SNCA and APOE exists that can explain the protein-level association, we investigated the genotypic interaction of APOE and SNCA in cohorts of PD, DLB, and iRBD. We analyzed genome-wide association study (GWAS) data from 5,229 PD patients and 5,480 controls, 2,610 DLB patients and 1,920 controls, and 1,055 iRBD patients and 3,667 controls. We used logistic regression interaction models across all 3 cohorts independently between the 1) top GWAS signals of SNCA SNPs and APOE haplotypes, 2) SNP x SNP and 3-way SNP interaction across the entire coding region plus 200kb flanking each gene. No significant interactions were found to be associated with any of the synucleinopathies after correction for multiple testing. Our results do not support a role for genetic interactions between APOE and SNCA across PD, DLB, and iRBD. Since the tested genetic variants affect the expression and function of these proteins, it is likely that any interactions between them does not affect the risk of PD, DLB and iRBD.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal Morbidity and Medically Assisted Reproduction Treatment Types: Evidence from the Utah Population Database","authors":"Alina Pelikh, KenjiKamiyama PhD, Mikko Myrskylä PhD, Michelle P Debbink PhD, PhD Alice Goisis","doi":"10.1101/2024.08.12.24311859","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311859","url":null,"abstract":"Study question: How are Medically Assisted Reproduction (MAR) treatments (Fertility enhancing drugs (FED), artificial/intrauterine insemination (AI/IUI)), assisted reproductive technology (ART) with autologous/donor oocytes) associated with maternal morbidity (MM)? Summary answer: More invasive MAR treatments (ART and AI/IUI) are associated with higher risk of MM, whilst less invasive treatments are not; this relationship is partially explained by higher prevalence of multifetal gestation and obstetric comorbidities in women undergoing more invasive treatment, but the persistent association suggests subfertility itself may contribute to maternal morbidity risk. What is known already: Women conceiving through MAR are at higher risk of MM, however, reported risks vary depending on the measurement of MM and data available on confounding. Study design, size, duration: Birth certificates were used to study maternal morbidity among all women giving birth in Utah, U.S., between 2009 and 2017 (N=460,976 deliveries); 19,448 conceived through MAR (4.2%). The MM outcome measure included the presence of any of the following: blood transfusion; unplanned operating room procedure; admission to ICU; eclampsia; unplanned hysterectomy; ruptured uterus. Participants/materials, setting, methods: Logistic regressions were estimated for the binary outcome (presence of any of the MM conditions). We assessed MM among women conceiving through MAR (overall and by type of treatment) compared to those conceiving spontaneously in the overall sample before and after adjustment for maternal socio-demographic characteristics (maternal age, family structure, level of education, Hispanic origin, parity), pre-existing maternal comorbidities (i.e., chronic hypertension, heart disease, asthma), multifetal gestation, and obstetric comorbidities (i.e., placenta previa, placental abruption, preterm delivery, cesarean delivery). Main results and the role of chance: Women conceiving through MAR had higher risk of MM; however, the magnitude of the association differed depending on the type of treatment. In the unadjusted models, more invasive treatments were associated with higher odds of MM: OR 5.71 (95% CI 3.50-9.31) among women conceiving through ART with donor oocytes, OR 3.20 (95% CI 2.69-3.81) among women conceiving through ART with autologous oocytes, and OR 1.85 (95% CI 1.39-2.46) among women conceiving through AI/IUI, whereas women conceiving through FED had similar risks of MM to compared to women conceiving spontaneously (SC), OR 1.09 (95% CI 0.91-1.30). The associations between MAR and MM were largely attenuated once multifetal gestation was accounted for. After controlling for obstetric comorbidities, the associations were further attenuated, yet the coefficients remained higher among women conceiving through ART with either donor oocytes OR 1.70 (95% CI 0.95-3.04) or autologous oocytes OR 1.46 (95% CI 1.20-1.78) compared to women conceiving spontaneously. In analyses limite","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"10 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TIDAL: Tool to Implement Developmental Analysis of Longitudinal data","authors":"A. Kwong, A. Edmondson-Stait, Eileen Xu, Ellen J. Thompson, Richard M. A. Parker, Ahmed Elhakeem, L. Romaniuk, Rebecca M. Pearson, Kate Tilling, Thalia C. Eley, McIntosh Andrew M, Heather C. Whalley","doi":"10.1101/2024.08.12.24311854","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311854","url":null,"abstract":"Motivation: Growth curve modelling is one method used to model trajectories of traits and behaviours over time. However, accessing, analysing and interpreting trajectories requires statistical expertise, thereby creating potential barriers for users to implement and understand longitudinal traits. TIDAL is a user-friendly research tool designed to facilitate trajectory modelling by improving access, analysis and interpretation of trajectory and longitudinal data. Implementation: TIDAL is available in two formats: an R package and an online Shiny application. The R package can be used offline, negating the need to upload potentially sensitive data. General features: TIDAL includes all the main steps of trajectory analysis including: 1) data preparation, (converting data from wide to long format); 2) data exploration, via basic plots and descriptive information; 3) analysis of trajectories using mixed effects modelling, interpretation of results, visualisation of trajectories, and extraction of key features (scores at different ages; area under the curve); and 4) interactions to derive population specific trajectories, combined with all the above. TIDAL is built with a simple graphical interface to guide users through each step. R syntax accompanies each step. Availability: Both versions of TIDAL can be found here: [https://tidal-modelling.github.io/].","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"10 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal Morbidity and Medically Assisted Reproduction Treatment Types: Evidence from the Utah Population Database","authors":"Alina Pelikh, KenjiKamiyama PhD, Mikko Myrskylä PhD, Michelle P Debbink PhD, PhD Alice Goisis","doi":"10.1101/2024.08.12.24311859","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311859","url":null,"abstract":"Study question: How are Medically Assisted Reproduction (MAR) treatments (Fertility enhancing drugs (FED), artificial/intrauterine insemination (AI/IUI)), assisted reproductive technology (ART) with autologous/donor oocytes) associated with maternal morbidity (MM)? Summary answer: More invasive MAR treatments (ART and AI/IUI) are associated with higher risk of MM, whilst less invasive treatments are not; this relationship is partially explained by higher prevalence of multifetal gestation and obstetric comorbidities in women undergoing more invasive treatment, but the persistent association suggests subfertility itself may contribute to maternal morbidity risk. What is known already: Women conceiving through MAR are at higher risk of MM, however, reported risks vary depending on the measurement of MM and data available on confounding. Study design, size, duration: Birth certificates were used to study maternal morbidity among all women giving birth in Utah, U.S., between 2009 and 2017 (N=460,976 deliveries); 19,448 conceived through MAR (4.2%). The MM outcome measure included the presence of any of the following: blood transfusion; unplanned operating room procedure; admission to ICU; eclampsia; unplanned hysterectomy; ruptured uterus. Participants/materials, setting, methods: Logistic regressions were estimated for the binary outcome (presence of any of the MM conditions). We assessed MM among women conceiving through MAR (overall and by type of treatment) compared to those conceiving spontaneously in the overall sample before and after adjustment for maternal socio-demographic characteristics (maternal age, family structure, level of education, Hispanic origin, parity), pre-existing maternal comorbidities (i.e., chronic hypertension, heart disease, asthma), multifetal gestation, and obstetric comorbidities (i.e., placenta previa, placental abruption, preterm delivery, cesarean delivery). Main results and the role of chance: Women conceiving through MAR had higher risk of MM; however, the magnitude of the association differed depending on the type of treatment. In the unadjusted models, more invasive treatments were associated with higher odds of MM: OR 5.71 (95% CI 3.50-9.31) among women conceiving through ART with donor oocytes, OR 3.20 (95% CI 2.69-3.81) among women conceiving through ART with autologous oocytes, and OR 1.85 (95% CI 1.39-2.46) among women conceiving through AI/IUI, whereas women conceiving through FED had similar risks of MM to compared to women conceiving spontaneously (SC), OR 1.09 (95% CI 0.91-1.30). The associations between MAR and MM were largely attenuated once multifetal gestation was accounted for. After controlling for obstetric comorbidities, the associations were further attenuated, yet the coefficients remained higher among women conceiving through ART with either donor oocytes OR 1.70 (95% CI 0.95-3.04) or autologous oocytes OR 1.46 (95% CI 1.20-1.78) compared to women conceiving spontaneously. In analyses limite","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"36 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeted inference to identify drug repositioning candidates in the Danish health registries","authors":"A. W. Jung, I. Louloudis, S. Brunak, L. Mortensen","doi":"10.1101/2024.08.12.24311869","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311869","url":null,"abstract":"Electronic health records can be used to track diagnoses and drug prescriptions in large heterogeneous populations over time. Coupled with recent advances in causal inference from observational data, these records offer new opportunities to emulate clinical trials and identify potential targets for drug repositioning. Here, we run a hypothesis generating cohort study of Danes aged 50 to 80 years from 2001 to 2015 (n = 2,512,380), covering a total of 23,371,354 years of observations. We examine prescription drugs at ATC level-4 and their effect on 9 major disease outcomes. Using Bayesian time-varying Cox regression and longitudinal minimum loss estimation, our analysis successfully reproduces known drug-disease associations from clinical trials, such as the reduction in the 3-year absolute risk of death associated with Statins (ATC:C10AA) -0.8% (95% CI =[-1.2%, -0.5%]) and -0.8% (95% CI =[-1.3%, -0.2%]) for females and males, respectively. Additionally, we discovered novel associations that suggest potential repositioning opportunities. For instance, Statins were associated with a reduction in the 3-year absolute risk of dementia by -0.3% (95% CI =[-0.5%, -0.1%]) for females and -0.2% (95% CI =[-0.4%, 0.1%]) for males. Furthermore, Biguanides (ATC:P01BB) stands out as a particularly interesting candidate with absolute risk reductions across various outcomes. In total, we identified 76 potential drug-disease pairs for further investigation. However, it should be stressed that the emulation of clinical trials here is solely of hypothesis generating nature and identified effects need to be corroborated with additional evidence, preferably from RTCs, as the risk of confounding by indication in this study is substantial. In summary, this study provides a large-scale screen of prescribed drugs and their effect on major debilitating disease in the Danish health registries. This provides an additional source of information that can be used in the search for possible repositioning candidates.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Detection of Mental Health Crises through AI-Powered Social Media Analysis: A Prospective Observational Study","authors":"Masab A. Mansoor, Dba, MD Kashif Ansari","doi":"10.1101/2024.08.12.24311872","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311872","url":null,"abstract":"Background: Early detection of mental health crises is crucial for timely intervention and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multi-modal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over a 12-month period. Performance was evaluated using standard metrics and validated against expert psychiatric assessment. Results: The AI model demonstrated high accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges including privacy concerns, potential stigmatization, and cultural biases need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration with existing mental health services, and development of personalized, culturally-sensitive models. Keywords: artificial intelligence, mental health, crisis detection, social media analysis, early intervention","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"8 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}